<p id="isPasted">Embedded World 2024, held at Nuremberg Messe from April 8 to 11, was a brilliant event for all engineers, designers, and researchers involved in embedded systems.&nbsp;</p><p>With almost a thousand exhibitors and a huge schedule of related events, Embedded World has really lived up to its reputation of showcasing the industry's cutting-edge.&nbsp;</p><p>Wevolver team members took the chance to meet engineers from all over the world. Here are some of the highlights.&nbsp;</p><h2>OKdo: LLMs at the edge.&nbsp;</h2><p>At Embedded World 2024, <a href="https://www.wevolver.com/profile/okdo" target="_blank" rel="noopener noreferrer">OKdo</a> - a key player in single-board computing and IoT solutions - &nbsp;showcased its significant advancements in industrial computing technologies. One of the highlights was the display from LiquidAI. Liquid AI is developing a new generation of AI foundation models built from first principles - going beyond generative pre-trained Transformers (GPTs). Liquid neural net technologies unlock efficiency, robustness, explainability, and state-of-the-art performance. Generative AI on the edge (for example on a Raspberry Pi 5 or Rock 5a) becomes possible.&nbsp;</p><p><span class="fr-img-caption fr-fic fr-dib" style="width: 790px;"><span class="fr-img-wrap"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzEzOTUxMTA5ODUwLU9LIHN0YW5kLmpwZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 100%px;" class="fr-fic fr-dib"><span class="fr-inner">The OKdo Embedded World stand. Credit: OKdo.</span></span></span></p><h2>Enabling cutting-edge PCB design</h2><p>Altium, a PCB design software company, &nbsp;introduced its new enterprise-grade system, Electronics Lifecycle Management (ELM), a system for enterprise-level hardware design and development, which was a featured presentation in the Exhibit Hall. The booth experience also featured live demos of solutions, including Altium Designer, Altium 365, Nexar API, and Octopart. Altium is transforming electronic development by allowing anyone in the electronics industry to connect with team members and partners and with solutions and data to work more productively.&nbsp;</p><p>“Altium is uniquely positioned to reshape the future of electronics development and production. We were extremely excited to share our vision of a transformed industry ― which is fast becoming a reality ― with the world’s leaders in embedded technology at Embedded World 2024.” – VP Enterprise Marketing, Altium, Naomi Hamels&nbsp;</p><p>See Altium in action below:&nbsp;<span contenteditable="false" draggable="true" class="fr-video fr-deletable fr-fvc fr-dvb fr-draggable"><iframe width="640" height="360" src="https://www.youtube.com/embed/aZeWMmDGFKg?&amp;wmode=opaque&amp;rel=0&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true" data-gtm-yt-inspected-9="true" id="253684619" title="Altium at Embedded World 2024"><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span></iframe></span></p><h2>Exploring NXP's advancements in motor control and TSN communication&nbsp;</h2><p>At Embedded World 2024, NXP showcased advancements in smart edge technologies aimed at improving device automation and connectivity. They introduced new technologies and products for various markets, including automotive, industrial, and smart home sectors. Demonstrations highlighted NXP's solutions for software-defined vehicles, smart home interoperability, and industrial automation. The event also featured expert-led tours and demonstrations exploring AI, edge computing, and more.</p><p><span contenteditable="false" draggable="true" class="fr-video fr-deletable fr-fvc fr-dvb fr-draggable"><iframe width="640" height="360" src="https://www.youtube.com/embed/fFeo0q6AdWA?&amp;feature=youtu.be&amp;wmode=opaque&amp;rel=0&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true" data-gtm-yt-inspected-9="true" id="597216751" title="Wevolver at Embedded World: NXP Semiconductors"><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span></iframe></span></p><h2>The latest in automotive local dimming control with Synaptics</h2><p>AT EW 2024, Synaptics launched its <a href="https://www.synaptics.com/products/embedded-processors" target="_blank" rel="noopener noreferrer">Astra platform</a> of AI-native embedded processors designed to enhance the capabilities of Internet of Things (IoT) devices. This platform focuses on providing multi-modal, AI-enabled microprocessors (MPUs) and microcontrollers (MCUs) that are optimized for power efficiency and high performance. Astra supports a unified software experience across various operating systems and includes an adaptive AI framework, which facilitates the integration of AI into devices. It also offers robust connectivity solutions, such as Wi-Fi and Bluetooth, and is designed to help developers easily incorporate AI features into their products.</p><p>During our visit, we also got to see their SmartBridge technology live. This advanced automotive display processor is a local dimming IC that supports large, high-contrast, high-resolution automotive LCDs while consuming lower overall power and cost. SmartBridge can handle up to 30-inch displays and 6k resolutions. It leverages a proprietary algorithm to analyze incoming images and control the brightness of each LED in the array.&nbsp;</p><p><span contenteditable="false" draggable="true" class="fr-video fr-deletable fr-fvc fr-dvb fr-draggable"><iframe width="640" height="360" src="https://www.youtube.com/embed/xGelGpfUJFw?&amp;wmode=opaque&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true" data-gtm-yt-inspected-9="true" id="506257900" title="Wevolver at Embedded World: Synaptics Incorporated"><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span></iframe></span></p><h2>Code reading with MVTec Software</h2><p>At the Embedded World 2024, <a href="http://www.mvtec.com" target="_blank" rel="noopener noreferrer">MVTec Software</a> showcased its extensive portfolio of machine vision software, prominently featuring their products HALCON and MERLIC. These products are designed to integrate seamlessly with various hardware configurations, supporting a broad spectrum of operating systems, AI accelerators, CPUs, and embedded boards. The company emphasized the adaptability of their software to different industrial needs, making it suitable for smart cameras, sensors, and other embedded vision applications.</p><p>MVTec's presentation revolved around the theme "Embedded Vision = YOUR Hardware + OUR Software," underscoring the customizable nature of their solutions to enhance hardware capabilities with sophisticated vision software. This approach not only facilitates enhanced performance and compatibility across different machine vision standards like GigE Vision, GenICam, and USB3 Vision but also promotes quicker time-to-market for products developed using their systems. The technologies highlighted include advanced features for code reading, optical character recognition (Deep OCR), and deep learning applications such as anomaly detection.</p><p><span contenteditable="false" draggable="true" class="fr-video fr-deletable fr-fvc fr-dvb fr-draggable"><iframe width="640" height="360" src="https://www.youtube.com/embed/CHitadg5uKg?&amp;wmode=opaque&amp;rel=0&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true" data-gtm-yt-inspected-9="true" id="37302711" title="Wevolver at Embedded World: MVTec Software Demo"><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span></iframe></span></p><h2>The 2024 State of Edge AI Report</h2><p>At Embedded World we were thrilled to share the 2024 State of Edge AI Report with you. &nbsp;This report represents months of hard work, research, and dedication to exploring the dynamic world of Edge AI applications across industries.</p><h3>Report Highlights:</h3><p>#1: Industry-specific insights– Each industry is covered by a dedicated chapter that provides industry-specific insights, descriptions, and examples. ,</p><p>#2: Real-life case studies – Get comprehensive insights from featured sections of real-world case studies.&nbsp;</p><p>#3: A look into the future - The report also provides a peek into the exciting generative AI technology and its convergence with edge computing before delving into the challenges still hindering Edge AI today.</p><p>We hope you download the report below!&nbsp;</p><p><span contenteditable="false" draggable="true" class="fr-video fr-deletable fr-fvc fr-dvb fr-draggable"><iframe width="540" height="665" src="https://c8056756.sibforms.com/serve/MUIFAIvLRE9FbYyICZVAlYl6oaSYA1y_4gbHWS82SbbrrHstSB6VMs2aj57Tvg0e16L_UZVprsukgYH2yB927lpe-0uPg3ZEAhJiDPRQb2_oMEFQLYiEcG6zXHV6DwzxZPaJo25g3EzCRsYZ5z3ey0dkbIZE_M-AQ1kRN3i2_dq-vjEv7FQC29p_-Vu8tD5vbyaerZZF9IYqTQ8D" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true" data-gtm-yt-inspected-9="true"><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span></iframe></span></p><p><br></p>

Wevolver was present at the recent Embedded World event in Nuremberg to launch the 2024 Edge AI report. 


Embedded World 2024: Trends, Tech, and Trailblazers

<p dir="ltr" id="isPasted">Our new report, <strong>AI at the Edge: Technology Accelerated by Wi-Fi Services</strong>, dives deeply into the convergence of Wi-Fi services and Edge AI and explores how a technology model like WaaS can accelerate the adoption of AI at the edge.&nbsp;</p><p dir="ltr">Below is an excerpt from the final chapter of the report. To read the full report, including in-depth case studies, download it now.</p><p dir="ltr"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzExMTI0MjE4NTYyLVJlbGF5IDIucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib"></p><p dir="ltr"><span contenteditable="false" draggable="true" class="fr-video fr-deletable fr-fvc fr-dvb fr-draggable"><iframe width="540" height="305" src="https://c8056756.sibforms.com/serve/MUIFAIgRr7nh2u88k57MUBWp43e1lkOKIXq3Z9dxFiC8kXdgaxMxkxsZKsum_gd4u7Qq4_1n-e3n6hH4X_Nw12RLp7SUWPiUkUUgLHhq84lH8VfbGGhwuM1Hvkr60WpcS1SyyBLR-7ErbMPEfKrwqo_-_-CHDFwvTGW-swKGBpTKYdEO-rdhIbTFZyt7qA8fH9zg-zqRf6Li7cOQ" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe></span><br></p><p dir="ltr" id="isPasted">The next years of this decade are going to be extremely exciting for AI, particularly Edge AI. And with Wi-Fi Services like Relay2 revolutionizing the access point market, the future is going to be very promising indeed. The surging demand for AI applications that offer real-time, low-latency, and privacy-conscious capabilities will usher in a wave of Edge AI implementations. These deployments are poised to become increasingly precise and efficient, capitalizing on advancements in communication networks, such as Wi-Fi 6, coupled with innovations in artificial intelligence (such as data-efficient AI) and the Internet of Things (IoT).&nbsp;</p><p dir="ltr">This transformative landscape underscores the pivotal role played by researchers, industry leaders, and open-source communities in steering the trajectory of Edge AI development. The evolution of efficient Edge AI applications relies on the progress of various technologies designed to support edge devices and enhance the computational efficiency of AI functions, including Wi-Fi services.</p><h3 dir="ltr">Increased Adoption &amp; Integration with Edge AI</h3><p dir="ltr">As the demand for real-time processing and low-latency applications continues to surge, the integration of Edge AI with Wi-Fi services will follow suit. The agility and scalability of Wi-Fi seamlessly complement the requirements of edge computing and AI. For instance, the latest generation of Wi-Fi, Wi-Fi 6, promises much faster data transfer speeds that can reach up to five times the speeds of Wi-Fi 5. This is complemented by reduced power usage, increased capacity, and enhanced performance, providing higher connection reliability in environments with multiple connected devices. This is where Relay2 sets a standard with its Wi-Fi services, pushing for innovation that leverages advancements like Wi-Fi 6. With a dynamic system that ensures flexibility and security, Relay2 offers virtual access control and simultaneous HD video sessions that can support up to 128 sessions. This is further supported by high performance and management of heavy traffic, capitalizing on an average throughput DL that exceeds 2 Gbps and handles 1,024 clients concurrently.</p><p dir="ltr">This is bound to grow and enhance the adoption and integration with Edge AI in order to enrich the user experience. By implementing AI at the edge for efficient data processing and leveraging Relay2’s proactive real-time analysis and natural language adaptability, businesses and organizations can elevate their user experience beyond merely improving connection reliability. This synergy allows for efficient processing closer to data sources, reducing latency and enhancing overall system responsiveness.</p><p dir="ltr">With Wi-Fi 7 being the next generation of Wi-Fi, we could see an even more seamless and immersive user experience and an advanced Edge AI performance, building on its new features such as multi-link operation (MLO), 320 MHz superwide channels, and a 4k quadrature amplitude modulation (QAM). This convergence of state-of-the-art communications and information technologies, such as cloud-native Wi-Fi services, edge computing, IoT, and AI, is driving a massive leap in management efficiency and social productivity. The hybrid edge-cloud architectures and applications are likely to surge in the very near future, ensuring not only high speed and efficiency but also the ability to run multi-modal generative AI and language models at the edge . In the second half of this decade, we will arguably witness an unprecedented increase in the adoption of Edge AI across numerous applications, rendering the technology mainstream.</p><h3 dir="ltr">Computer Vision &amp; the Impact of Edge AI and Wi-Fi</h3><p dir="ltr">One area where the convergence of Edge AI and Wi-Fi services is poised to have a high impact is computer vision applications. With the capability to process data locally, Wi-Fi-empowered edge devices can execute intricate computer vision tasks with unparalleled speed and efficiency. This transformative shift not only minimizes reliance on centralized cloud servers but also allows positioning systems to optimize energy and costs, enabling applications such as facial recognition, object detection, and automated surveillance to operate seamlessly in real time.</p><p dir="ltr">With localization techniques such as Wi-Fi-based positioning systems (WPS) and the advent of convolutional neural networks (CNNs), we can detect and extract spatial patterns from visual data in a remarkable manner. This has the potential to transform industries, including healthcare, retail, automotive, and traffic management. Combining the high-processing capabilities of cloud computing with the real-time features of edge computing and the connectivity enhancements brought about by Wi-Fi technologies can enable AI to lift computer vision to a different level. Consider industrial automation in large factories and warehouses, smart cities with optimized traffic control, retail centers and shopping complexes with enhanced services and store layouts based on monitored customer behavior, and even remote medical services and patient monitoring that enable accurate diagnosis in real time. All these developments can take place by leveraging computer vision that is powered by AI at the edge.</p><p dir="ltr">While there are still challenges standing before computer vision today, such as high processing power and high-quality data, the potential that Edge AI brings, supported by Wi-Fi capabilities that ensure fast and seamless data transfer - even in remote areas where accessibility would otherwise be difficult - will propel computer vision and enable it to be a much more potent and ubiquitous technology.&nbsp;</p><p dir="ltr">With on-device inference capabilities and functionalities that enable computer vision within edge and mobile devices, image and pattern recognition can improve significantly, taking advantage of edge computing’s reduced latency, enhanced security, and minimized costs. Integrating AI into the mix enables even low-cost cameras to act as smart cameras, with deep learning methods enabling better identification and interpretation of data patterns. This is further enhanced by leveraging data from multiple devices connected via Wi-Fi, which provides AI with more comprehensive data, reinforcing its precision and understanding.</p><h2 dir="ltr">Conclusion: Wi-Fi-Powered Edge AI - A Technology for the Future</h2><p dir="ltr">Wi-Fi and Edge AI represent a technological paradigm shift with far-reaching implications. The convergence of Wi-Fi with Edge AI is not just a technological evolution but a fundamental reshaping of the digital landscape. As this symbiotic relationship matures, it propels us towards a future where Edge AI is not just a technology but an integral and indispensable part of our interconnected world. The potential for transformative applications is immense, urging both developers and sellers to explore the uncharted territories of innovation and contribute to the unfolding narrative of Edge AI's promising future.</p><p dir="ltr">This not only addresses the computational needs of Edge AI but also aligns with the growing trend of connected devices in smart buildings, industrial settings, healthcare, retail, and smart cities. The integration of Edge AI with Wi-Fi services brings forth a boost in performance, user experience, and security measures, ensuring that sensitive data is processed locally. This transformation leads to the development of robust, always-on systems capable of meeting the stringent privacy requirements of contemporary applications and the ever-growing need for better performance, flexibility, scalability, and reliability.</p><p dir="ltr">Throughout this report, we explored the capabilities and impact of Edge AI, especially when coupled with Wi-Fi services. We highlighted the valuable insights provided by Relay2, the leading Wi-Fi technology provider for the Edge AI market. Relay2's Service Points technology is designed to deliver exceptional application performance while enhancing security, privacy, and scalability at the edge.&nbsp;</p><p dir="ltr">The Relay2 ServiceEdge Platform harnesses the power of Wi-Fi Services with Edge Computing to create a digital world where application performance and user experience not only remain uncompromised but are also enhanced. We also dove into particular applications and reviewed some use cases of Wi-Fi services edge computing. And finally, we took a look at the future of these technologies and how they will impact our businesses and way of living in the coming years. All this could not have been done without the support of Relay2 in developing this report.&nbsp;</p><p dir="ltr">Relay2 is committed to empowering its partners by providing comprehensive, innovative solutions to ensure mutual success leveraging Wi-Fi Services with Edge Computing and the Relay2 ServiceEdge Platform. Therefore, developers and sellers are encouraged to partner with Relay2 to unlock the full potential of technology and shape a seamless digital future. The convergence of Wi-Fi with Edge computing can reshape the digital technology and business landscape, and Relay2 is at the forefront of this transformation.</p><h2 dir="ltr">Acknowledgments</h2><p dir="ltr">This report is the result of collaborative efforts between Wevolver and Relay2. Initial input was graciously provided by Relay2’s Eric Chen and Daniel Kwong, sharing their insights, expertise, and case studies, creating a robust technical substructure for the report. Then, the author further expanded upon this information to develop a comprehensive and insightful text, supported by the content and design team at Wevolver. Thank you to all contributors and supporters, and special thanks to Relay2.</p><p dir="ltr">Download the full report with custom images, case studies and more, below.&nbsp;</p><p dir="ltr"><span contenteditable="false" draggable="true" class="fr-video fr-deletable fr-fvc fr-dvb fr-draggable"><iframe width="540" height="305" src="https://c8056756.sibforms.com/serve/MUIFAIgRr7nh2u88k57MUBWp43e1lkOKIXq3Z9dxFiC8kXdgaxMxkxsZKsum_gd4u7Qq4_1n-e3n6hH4X_Nw12RLp7SUWPiUkUUgLHhq84lH8VfbGGhwuM1Hvkr60WpcS1SyyBLR-7ErbMPEfKrwqo_-_-CHDFwvTGW-swKGBpTKYdEO-rdhIbTFZyt7qA8fH9zg-zqRf6Li7cOQ" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe></span><br></p>

The coming years will see a surge in Edge AI advancements, driven by real-time, efficient, and privacy-focused AI applications, alongside innovations in Wi-Fi and IoT, marking a promising future shaped by researchers, industry leaders, and open-source contributions.

AI at the Edge Report. Chapter 4: Future of Edge AI with Wi-Fi Technology

<p dir="ltr" id="isPasted">Our new report, <strong>AI at the Edge: Technology Accelerated by Wi-Fi Services</strong>, dives deeply into the convergence of Wi-Fi services and Edge AI and explores how a technology model like WaaS can accelerate the adoption of AI at the edge.&nbsp;</p><p dir="ltr">Below is an excerpt from Chapter 3 of the report. To read the full report, including in-depth case studies, download it now.</p><p dir="ltr"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1710514655459-Relay+2.png" style="width: 100%px;" class="fr-fic fr-dib"></p><p dir="ltr"><span contenteditable="false" draggable="true" class="fr-video fr-deletable fr-fvc fr-dvb fr-draggable"><iframe width="540" height="305" src="https://c8056756.sibforms.com/serve/MUIFAIgRr7nh2u88k57MUBWp43e1lkOKIXq3Z9dxFiC8kXdgaxMxkxsZKsum_gd4u7Qq4_1n-e3n6hH4X_Nw12RLp7SUWPiUkUUgLHhq84lH8VfbGGhwuM1Hvkr60WpcS1SyyBLR-7ErbMPEfKrwqo_-_-CHDFwvTGW-swKGBpTKYdEO-rdhIbTFZyt7qA8fH9zg-zqRf6Li7cOQ" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe></span><br></p><p id="isPasted">The applicability of Wi-Fi Edge AI is fairly expansive and is growing by the day, spanning various industries and application areas. The most central aspects of this intersection of technologies are the real-time insights they provide, how these insights impact the speed and type of response, and how they are communicated across the network. Whether it is in smart healthcare, education, retail, or other application areas, Wi-Fi-enabled Edge AI is redefining how technology is leveraged in real time. Relay2 utilizes several Edge AI platforms to enhance the functionality of AI applications on the edge. In this chapter, we will take a look at some interesting use cases of Wi-Fi Edge AI, and we will see the impact of this technology convergence and the new opportunities it is opening in new and emergent technological spaces.</p><h2>Education / Smart Campus</h2><p>Wi-Fi Edge AI plays an important role in education-related applications, among which is remote learning integration, facilitating seamless and interactive remote education and bridging the gap between on-site and remote learning experiences. With localized content engagement pattern analysis and personalized experiences for students, Wi-Fi services and edge computing can help enhance and refine education content delivery.&nbsp;</p><p>Campus facilities can also benefit significantly from integrating such technologies, especially in terms of security and surveillance, where Wi-Fi Edge AI can help analyze surveillance videos locally and in real time to recommend timely actions for security staff. It also powers use cases like attendance automation and traffic patterns, streamlining attendance tracking for educators and student flow analysis for operation enhancement.&nbsp;</p><p>Another use case of Wi-Fi and edge computing can be seen in revenue generation, in which they enable intelligent, location-based advertising through audience behavior analysis for commercial partners. By leveraging these technologies, educational institutions can help promote campus services better, boost product marketing across campus premises and beyond, and enable efficient digital transactions.</p><p>One example of applying Wi-Fi and edge computing in education is by WENet, an OEM partner of Relay2 in Vietnam. In order to support the University of Transport Technology in Vietnam in developing a digital technology infrastructure platform, WENet deployed Relay2’s Wi-Fi-enabled edge computing solution, making use of the advantages provided by Relay2 ServiceEdge Platform compared to conventional Wi-Fi networks. From stable bandwidth to fast processing speed, short deployment time, minimal service interruption time, and optimal operating costs, the features brought about by Relay2’s ServiceEdge Platform were highly effective. As a result, over 17 thousand students across three campuses were able to benefit from this platform, especially in terms of the following three key functions:</p><ul><li>Digital signage interacting with mobile devices to support two-way communication (university-to-student communication and student responses to university requests)</li><li>Students’ access to digital education materials from Relay2’s Service Points</li><li>Students’ ability to order food digitally from the cafeteria</li></ul><p>Such functions have not only enabled students to benefit from a Wi-Fi-powered edge computing platform but also helped collect analytic data for future AI processing at the edge. Read more about the WENet use case on the Relay2 website.</p><h2>Smart Healthcare</h2><p>Undoubtedly, one of the most important aspects of healthcare is real-time monitoring and real-time decision-making. However, as we have seen over the recent years with pandemic-caused crises, healthcare workers are more often than not overworked, and hospitals are resource-limited, which can limit their abilities to provide timely care. In order to ensure patient safety and health within hospital spaces and elsewhere, it is crucial to track, monitor, analyze, and act in real-time. All this can be made available by incorporating Edge AI and Wi-Fi. Whether it is remote monitoring and management, patient activity tracking, patient care, or even assistance during healthcare crises, hospitals, and medical personnel can benefit significantly from integrating Wi-Fi Edge AI into their processes.</p><p>Smart healthcare comes in the form of connected healthcare, where AI-based smart devices, healthcare management software, and digital instruments are integrated into a “connected” healthcare system using a network protocol like Wi-Fi. As such, medical personnel would have access to all the medical-related information they need to provide the patients with the necessary medical treatment in the most effective and convenient way possible. This has been made possible by utilizing Edge AI and Wi-Fi technologies to empower the Internet of Medical Things (IoMT). In other words, healthcare personnel can leverage the low-latency capabilities of Edge AI processing and the ubiquity of Wi-Fi connectivity to access real-time data from diverse medical sensors, enabling timely decision-making and interventions and fostering a proactive approach to patient care.</p><p>Consider intensive care units (ICUs), operating rooms, or other healthcare spaces, the use of Edge AI and Wi-Fi can augment the work of doctors, nurses, and clinicians by minimizing limitations, redundancies, and time allocated to activities away from direct patient care (such as documentation). As a result, medical workers can benefit from an emerging ambient intelligence , powered by edge computing, AI capabilities, and Wi-Fi. This can even be expanded to daily living spaces, which would support independent living, chronic disease management, mental health, and physical rehabilitation, particularly of elderly individuals.</p><h2>Smart Retail</h2><p>Global retail is almost always in flux, and both retailers and suppliers are grappling with the complexities of ever-changing consumer demand, behavior, and expectations. The continuous shifts in these dynamics create uncertainties in predicting future trends and exert significant stress on international supply chains and store operations. Navigating these challenges necessitates real-time adaptability to stay ahead in the retail industry.</p><p>Edge AI emerges as a potent solution, offering retailers the capability to swiftly and precisely forecast daily demand while automating crucial aspects of supply chain logistics, customer engagement, and operational agility. This not only addresses challenges related to in-store supply management but also tackles issues in last-mile delivery costs, aligning with consumer expectations regarding timely deliveries.</p><p>Within physical stores, retailers leverage Edge AI to combat issues such as shrinkage and stockouts, optimizing inventory management. Simultaneously, the application of Edge AI and Wi-Fi contributes to the creation of frictionless in-store experiences while prioritizing the health and safety of both employees and shoppers. The integration of AI in retail is thus transforming operations, offering a multifaceted approach to address the intricate challenges posed by the dynamic retail landscape.</p><p>D.Phone, one of the largest cell phone retailers out of China, has deployed in-store Wi-Fi to enhance its customer experience and promote engagement as a way to offer a differentiated experience that would distinguish its &nbsp;D.Phone stores from competitors. They opted for Relay2’s wireless broadband solution, taking advantage of its unified native-cloud wireless networking and edge-service delivery.</p><p>By leveraging Relay2 access points, D.Phone was able to offer high-quality customer experiences and deliver value-added services in-store. With its fast and reliable coverage in demanding environments like customer-dense spaces, including shopping centers and large venues, Relay2 access points enabled D.Phone to meet its customers' expectations and offer them the high-speed connectivity they desire. Moreover, with Relay2’s Edge services, D.Phone was able to run sales-generating applications and computation-intensive services locally and place content right at its &nbsp;D.Phone customers’ fingertips, thus reducing content download time to just seconds. This helped streamline the customer purchasing experience and circumvent bandwidth bottlenecks. It even enabled D.Phone to deliver new services to its customers, including app delivery and couponing. This has displayed an accelerated go-to-market process for D.Phone’s digital merchandise and a reinforced branding strategy. Read more about the D.Phone use case on Relay2’s website.</p><blockquote><p>“Relay2’s plug-and-play access points and cloud-based platform [have] given us a solution that lowers the cost of deployment and management.” - Wu Huan, CIO, D.Phone</p></blockquote><h2>Smart Buildings</h2><p>With the recent growth in smart homes and building automation systems, edge computing, AI, and Wi-Fi have become increasingly popular in this space. The integration of Edge AI with sustainable architecture and smart buildings has been rapidly gaining momentum and transforming the manner in which we construct and interact with our built spaces. With advantages like enhanced energy efficiency, optimized systems, personalized occupant comfort, and streamlined building maintenance, smart buildings are leveraging the real-time analysis capabilities of Edge AI and its integration with IoT and developing potential for more sustainable architecture and digital integration.</p><p>Acting as a centralized intelligence hub, AI processes and interprets data obtained from an array of edge devices (IoT devices) such as sensors and actuators scattered throughout the building. This network of devices forms a comprehensive information system, providing valuable insights into various aspects of the building's operations. Through the implementation of AI algorithms, smart buildings gain the ability to efficiently monitor and regulate crucial functions like lighting, heating, ventilation, air conditioning, security, and energy consumption. Such an integration results in an environment that is not only responsive but also adaptive, catering to the dynamic needs and preferences of the building occupants.</p><p>This can be further supported by Wi-Fi connectivity and cloud-based Wi-Fi services, in particular. Cloud-based Wi-Fi services enable centralized management and control of Wi-Fi networks. This centralized approach simplifies the deployment and maintenance of Wi-Fi connectivity across the building, ensuring a more streamlined and scalable infrastructure. Furthermore, the cloud facilitates the storage and analysis of the vast amounts of data generated by IoT devices in Smart Buildings. AI algorithms require substantial data inputs to make informed decisions, and cloud-based services offer the computational resources needed for processing and interpreting this data. It enables more sophisticated analytics, allowing for better insights into building operations, occupant behaviors, and energy consumption patterns.&nbsp;</p><p>Similarly, the cloud's scalability can easily accommodate the dynamic nature of Smart Buildings, especially when the number of connected devices grows or changes. Just as importantly, cloud-based services enable remote monitoring, management, and updates of Wi-Fi networks and connected devices. This remote accessibility is invaluable for building administrators, allowing them to troubleshoot issues, implement updates, and optimize network performance without physically being on-site. In this regard, smart buildings can benefit meaningfully from Relay2’s innovative solutions. With advanced networking capabilities, Wi-Fi Edge Computing power, and intelligent analytics, users of Relay2’s ServiceEdge platform can ensure seamless connectivity and optimized operational efficiency.</p><h2>Smart Agriculture</h2><p>The Internet of Things (IoT) and edge computing have influenced the agricultural and farming industries quite extensively over the past decade, especially with the rapid rise of low-power wide-area networks (LPWAN) . Wireless communication protocols are, in fact, a vital part of smart agriculture systems, especially since the fields cover large areas that necessitate system components to be placed at remote locations. The integration of Wi-Fi and edge computing has been key to expanding the digital functionalities in agriculture, especially with the advent of AI at the edge, thus increasing the possibilities of smart agriculture over large areas.</p><p>One type of smart agriculture that has been propelled by Edge AI and Wi-Fi services is precision farming. By deploying sensors and cameras in fields, farmers can gather real-time data on crucial factors such as weather patterns, soil conditions, crop growth, and pest infestations. This localized data processing enables farmers to modify and regulate their irrigation, fertilization, and pest control without delay, leading to an increase in crop yields and resource efficiency. The implementation of edge devices equipped with sensors extends beyond crop monitoring to include livestock health and behavior. Wearable devices on animals, such as cattle, provide early indications of illness or oestrus, empowering farmers to take timely actions, resulting in improved animal well-being, reduced rates of mortality, and higher farm productivity all around.&nbsp;</p><p>Furthermore, the incorporation of Edge AI has paved the way for autonomous farming machinery, where tractors and drones equipped with edge devices can analyze field data in real time. This enables them to refine their routes, control planting depths, and identify and eliminate weeds in an autonomous manner. Not only does autonomous farming machinery reduce labor costs, but it also minimizes the use of chemicals, aligning agriculture with more eco-friendly practices. This is further empowered with the integration of Wi-Fi Services, allowing for centralized, remote monitoring and seamless connectivity between edge devices across large fields and in remote areas. The seamless integration of Edge AI and Wi-Fi technologies is proving transformative in making agriculture smarter, more efficient, and more sustainable over expansive agricultural landscapes.</p><p>Relay2 offers smart farms state-of-the-art solutions that can help transform crop production, efficiency, and sustainability. These solutions seamlessly connect and monitor agricultural devices, collect real-time data, and leverage advanced analytics for informed decision-making. By optimizing resources, automating processes, and implementing precision farming, Relay2 empowers farmers to maximize yield and sustainability.</p><hr><p>This was an excerpt from Chapter 3 of the <strong>AI at the Edge: Technology Accelerated by Wi-Fi Services</strong> report. If you would like to read the full report, including in-depth case studies, you can download it below. &nbsp;</p><p><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1710514806917-Relay+2.png" style="width: 100%px;" class="fr-fic fr-dib"></p><p><span contenteditable="false" draggable="true" class="fr-video fr-deletable fr-fvc fr-dvb fr-draggable"><iframe width="540" height="305" src="https://c8056756.sibforms.com/serve/MUIFAIgRr7nh2u88k57MUBWp43e1lkOKIXq3Z9dxFiC8kXdgaxMxkxsZKsum_gd4u7Qq4_1n-e3n6hH4X_Nw12RLp7SUWPiUkUUgLHhq84lH8VfbGGhwuM1Hvkr60WpcS1SyyBLR-7ErbMPEfKrwqo_-_-CHDFwvTGW-swKGBpTKYdEO-rdhIbTFZyt7qA8fH9zg-zqRf6Li7cOQ" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe></span><br></p>

Wi-Fi-enabled Edge AI is redefining how technology is leveraged in real time.

AI at the Edge Report. Chapter 3: Applications & Case Studies

<p dir="ltr" id="isPasted">BrainChip Holdings Ltd is a company that specializes in neuromorphic computing. They're known for their innovative approach and strategic vision in the field of artificial intelligence (AI). Their technology aims to redefine how AI is implemented at the edge. This article shares the highlights from their latest investor podcast to provide a holistic view of the company's achievements, strategies, and future direction.</p><h4 dir="ltr">A Vision for the Future</h4><p dir="ltr">BrainChip is committed to advancing neuromorphic computing, a cutting-edge field that mimics the neural architecture of the human brain. This approach promises unprecedented efficiency and performance in AI applications, particularly in edge computing scenarios where power and speed are critical.&nbsp;</p><p dir="ltr">The recent addition of Dr. Tony Lewis to the BrainChip leadership team underscores this commitment. With a rich background in AI, engineering, and neuromorphic technologies, Dr. Lewis brings a wealth of knowledge and a clear vision for the company's future.</p><p dir="ltr">n this journey. With an illustrious career spanning AI, engineering, and neuromorphic technologies, Dr. Lewis's expertise is set to steer BrainChip towards new horizons.</p><p dir="ltr">The announcement of Dr. Lewis joining the team heralds a new era for BrainChip, emphasizing the company's dedication to leading the edge in AI technology. Dr. Lewis brings a profound understanding of brain-inspired computing and robotics, with a track record of leading large research groups and pioneering innovations in the field. His appointment is not just a testament to BrainChip's current leadership in neuromorphic computing but also a clear indicator of its ambitious roadmap ahead.</p><p dir="ltr">Dr. Lewis's background is particularly noteworthy, with a PhD in brain-inspired computing in robotics, positioning him as a perfect fit for BrainChip's mission. His career has been a journey towards this role, marked by significant achievements in neomorphic and robotics research funded by the US government and industrial partners. His experience spans across defense, space, biomedical, environmental monitoring, mobile phones, and personal computers, highlighting a versatile and impactful career.</p><p dir="ltr">At Qualcomm, Dr. Lewis led the largest research project outside of wireless in corporate R&amp;D, focusing on brain-inspired computing. His foresight into the potential of deep learning and language models has been pivotal, anticipating the significant impact these technologies would have on the industry. His role at HP as the Global Head of the AI and Emerging Compute Lab further honed his skills, preparing him for the challenges and opportunities of being BrainChip's CTO.</p><p dir="ltr">Dr. Lewis's decision to join BrainChip was driven by the recognition of AI's golden age and the critical role of edge computing in shaping the future. He acknowledges the challenges of cloud processing, including its massive energy consumption and latency issues, underscoring the necessity for efficient, edge-based solutions. BrainChip, with its focus on neuromorphic computing, represents the cutting edge of this transition, offering solutions that are not only technologically advanced but also environmentally conscious and cost-effective.</p><h4 dir="ltr">Strategic Market Position and Competitive Edge</h4><p dir="ltr">BrainChip's belief in AI as a significant economic driver is matched by its strategic focus on edge computing. The company's neuromorphic computing platform offers a compelling alternative to traditional AI solutions, providing extreme efficiency, low power consumption, and real-time processing capabilities. This positions BrainChip uniquely in a market that is increasingly moving towards decentralized, edge-based computing solutions.</p><p dir="ltr">The competitive landscape of AI and neuromorphic computing is diverse, with several players vying for dominance. However, BrainChip sets itself apart through its comprehensive platform that supports a wide range of AI models and applications. The company's IP business model, which includes licensing fees and royalties, offers flexibility and scalability to partners and clients, further solidifying its market position.</p><h4 dir="ltr">Innovations and Achievements</h4><p dir="ltr">One of the key highlights from the investor podcast was BrainChip's focus on continuous innovation and product development. The introduction of the AKD1000 processor and the development of the next-generation IP are testament to the company's commitment to advancing neuromorphic computing. These technologies enable a broader range of use cases, from industrial automation to consumer electronics, by supporting more complex AI models and providing easier integration with existing workflows.</p><p dir="ltr">BrainChip's success is also reflected in its strategic partnerships and collaborations. The company has established relationships with industry leaders, research institutions, and technology developers to enhance its product offerings and expand its market reach. These partnerships not only validate BrainChip's technology but also provide valuable feedback and insights that drive further innovation.</p><h4 dir="ltr">Looking Ahead: Challenges and Opportunities</h4><p dir="ltr">As BrainChip continues to navigate the complex landscape of AI and neuromorphic computing, it faces both challenges and opportunities. The fragmented nature of the edge AI market presents a significant challenge, requiring a nuanced understanding of customer needs and industry trends. However, BrainChip's flexible business model and customer-focused approach position it well to adapt and thrive in this dynamic environment.</p><p dir="ltr">The potential competitive threat from major players like Nvidia moving into the edge computing space underscores the importance of maintaining a competitive edge through innovation and strategic partnerships. BrainChip's commitment to supporting standard machine learning workflows and its unique on-chip learning capabilities provide a strong foundation for sustaining its market leadership.</p><h4 dir="ltr">Conclusion</h4><p dir="ltr">BrainChip's journey in the AI industry is marked by bold visions, strategic innovations, and a relentless pursuit of excellence. The company's focus on neuromorphic computing not only sets it apart from competitors but also positions it as a pioneer in the future of AI. With a strong leadership team, strategic partnerships, and a commitment to continuous innovation, BrainChip is poised to lead the generational shift towards efficient, powerful AI at the edge. As the company navigates the challenges and opportunities ahead, its contributions to the field of AI and neuromorphic computing will undoubtedly continue to shape the technological landscape for years to come.</p>

Highlights from BrainChip's latest investor podcast provide a view of the company's achievements, strategies, and future direction.

BrainChip Podcast: Shareholder and innovation update

<p>BrainChip Holdings Ltd. has recently announced that it has opened pre-orders for the <a href="https://shop.brainchipinc.com/products/akida%E2%84%A2-edge-ai-box" target="_blank" id="isPasted">Akida Edge AI Box,</a>&nbsp; a significant milestone in the field of Edge AI. Through collaboration with VVDN Technologies, BrainChip has made significant progress in developing high-performance and energy-efficient artificial intelligence solutions that can be applied to various industries and use cases.</p><h4 dir="ltr" id="isPasted">Unveiling the Power of the Akida Edge AI Box</h4><p dir="ltr">The Akida Edge AI Box is engineered to harness the potential of neuromorphic computing, an innovative approach that mimics the human brain's neural networks. This technology enables the device to perform complex cognitive tasks more efficiently, using significantly less power than traditional computing methods.</p><p dir="ltr">Designed for versatility, the<a href="https://shop.brainchipinc.com/" target="_blank">&nbsp;Akida Edge AI Box&nbsp;</a>finds its application across various sectors, including retail, security, smart cities, automotive, and industrial processes. It aims to offer a solution that not only reduces operational costs but also enhances data privacy and security by processing data on the device, thereby minimising the need for constant cloud connectivity.</p><h4 dir="ltr">Key Features and Applications</h4><ul><li dir="ltr"><p dir="ltr">Ultra-Low Power Consumption: The device operates on minimal power, making it ideal for deployment in energy-sensitive environments.</p></li><li dir="ltr"><p dir="ltr">High-Performance AI Capabilities: With the ability to handle high-level computations and process vast amounts of data, the Akida Edge AI Box is perfect for complex applications like image processing and real-time analytics.</p></li><li dir="ltr"><p dir="ltr">Adaptability and Learning: Mimicking the plasticity of the human brain, the device can learn from new information and adjust its processing accordingly, enhancing its efficiency over time.</p></li><li dir="ltr"><p dir="ltr">Real-Time Processing: The Akida Edge AI Box excels in scenarios requiring immediate data analysis and decision-making, such as autonomous vehicles and smart surveillance systems.</p></li><li dir="ltr"><p dir="ltr">Robust and Resilient: Engineered to be fault-tolerant, the device ensures reliable operation even in challenging environmental conditions.</p></li></ul><h2 dir="ltr">Technical Specifications</h2><p dir="ltr"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1709550646582-Screenshot+2024-03-04+121015.png" style="width: 100%px;" class="fr-fic fr-dib"></p><p dir="ltr" id="isPasted">Priced at $799, the Akida Edge AI Box not only stands out for its technological prowess but also for its affordability, making advanced neuromorphic computing accessible to a broader range of industries.</p><p dir="ltr"><span class="fr-img-caption fr-fic fr-dib" style="width: 768px;"><span class="fr-img-wrap"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzA5NTUwNDI5NjI5LWJyYWluYm94LmpwZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 100%px;" class="fr-fic fr-dib"><span class="fr-inner">The Akida is best in class compared to current market products. Image credit: BrainChip.</span></span></span></p><h1 dir="ltr" id="isPasted">Bringing Edge AI Everywhere: Application examples</h1><h3 dir="ltr">Security and Surveillance</h3><p dir="ltr">the Akida Edge AI Box's capabilities have the potential to significantly transform the way monitoring and threat detection are carried out..&nbsp;Its ability to process data locally, with ultra-low latency, ensures real-time analysis of video feeds, detecting anomalies, unauthorized access, or suspicious behaviors instantly. This rapid processing can significantly enhance security measures in both public and private spaces, offering a more robust response to potential threats.</p><h3 dir="ltr">Smart Factories</h3><p dir="ltr">For smart factories, the Akida Edge AI Box brings the potential for improved automation, predictive maintenance, and operational efficiency. By processing data on-site, it can monitor equipment health in real-time, predict failures before they occur, and optimize manufacturing processes. This not only reduces downtime but also extends the lifespan of machinery, leading to significant cost savings and increased production efficiency.</p><h3 dir="ltr">Smart Retail</h3><p dir="ltr">In smart retail environments, the Akida Edge AI Box can personalize the shopping experience through real-time analytics. It can manage inventory, optimize store layouts based on customer behavior analytics, and offer targeted promotions to customers. This level of personalization and efficiency can enhance customer satisfaction and loyalty, driving sales and improving retail operations.</p><h3 dir="ltr">Smart Cities</h3><p dir="ltr">The Akida Edge AI Box can play a pivotal role in developing smart city infrastructure, from traffic management and waste management to energy conservation. Its capacity for edge processing allows for the real-time analysis of data from various sensors across the city, enabling intelligent decision-making that can improve city services, reduce congestion, enhance public safety, and lower environmental impact.</p><h3 dir="ltr">Warehouses</h3><p dir="ltr">In warehouses, the Akida Edge AI Box can streamline operations by optimizing logistics, managing inventory in real-time, and automating the tracking and dispatching of goods. This not only improves operational efficiency but also reduces the likelihood of errors, ensuring that the right products are always in stock and delivered on time.</p><p dir="ltr">By bringing AI processing capabilities directly to the edge, the Akida Edge AI Box enables these sectors to operate more efficiently, safely, and sustainably. Its introduction represents a significant step forward in the deployment of intelligent, secure, and customized devices and services for multi-sensor environments, catering to the specific needs of these diverse applications.</p><h2 dir="ltr">The Future of Edge Computing</h2><p dir="ltr">The collaboration between BrainChip and VVDN Technologies in developing the Akida Edge AI Box is a testament to the power of strategic partnerships in driving technological innovation. This device is set to redefine the landscape of Edge computing, ushering in a new era of intelligent, efficient, and autonomous systems.</p><p dir="ltr">By addressing the critical challenges of energy consumption, processing power, and data privacy, the Akida Edge AI Box is poised to play a crucial role in the evolution of Edge AI. Its launch not only meets the current market demand for more efficient computing solutions but also lays the groundwork for the future of intelligent devices and systems across a multitude of sectors.</p><p dir="ltr">As industries continue to seek smarter, more efficient technologies, the Akida Edge AI Box stands as a beacon of progress in the realm of Edge computing, promising a future where artificial intelligence is both profoundly intelligent and sustainably efficient.</p><p dir="ltr"><a href="https://shop.brainchipinc.com/" target="_blank">Order the&nbsp;Akida Edge AI Box here.</a></p>

BrainChip Holdings Ltd. Initiates Pre-Orders for the Akida Edge AI Box, Elevating Edge AI Capabilities through a Strategic Alliance with VVDN Technologies

Akida Edge AI Box: Edge Computing with Neuromorphic Technology

<p dir="ltr" id="isPasted">We are thrilled to share the <strong>2024 State of Edge AI Report</strong> with you.&nbsp;</p><p dir="ltr">This report represents months of hard work, research, and dedication to exploring the dynamic world of Edge AI applications across industries.</p><p dir="ltr"><strong>Report Highlights:</strong></p><ul><li dir="ltr"><p dir="ltr">#1:&nbsp;Industry-specific insights–&nbsp;Each industry is covered by a dedicated chapter that provides industry-specific insights, descriptions, and examples. ,</p></li><li dir="ltr"><p dir="ltr">#2: Real-life case studies – Get comprehensive insights from featured sections of real-world case studies.&nbsp;</p></li><li dir="ltr"><p dir="ltr">#3: A look into the future - The report also provides a peek into the exciting generative AI technology and its convergence with edge computing before delving into the challenges still hindering Edge AI today.</p></li></ul><p dir="ltr">Download the full report below.&nbsp;</p><p dir="ltr"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1710954579366-Mitsubishi+Chemical+Group++Growth+Garage++Open+Innovation+Program+%286%29.png" style="width: 100%px;" class="fr-fic fr-dib"></p><p dir="ltr"><span contenteditable="false" draggable="true" class="fr-video fr-deletable fr-fvc fr-dvb fr-draggable"><iframe width="540" height="505" src="https://c8056756.sibforms.com/serve/MUIFAIvLRE9FbYyICZVAlYl6oaSYA1y_4gbHWS82SbbrrHstSB6VMs2aj57Tvg0e16L_UZVprsukgYH2yB927lpe-0uPg3ZEAhJiDPRQb2_oMEFQLYiEcG6zXHV6DwzxZPaJo25g3EzCRsYZ5z3ey0dkbIZE_M-AQ1kRN3i2_dq-vjEv7FQC29p_-Vu8tD5vbyaerZZF9IYqTQ8D" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe></span><br></p><h2 dir="ltr">Report Introduction</h2><p dir="ltr">In the midst of all the technological advancements happening today, one term has taken over almost every discourse about technology: artificial intelligence (AI). Once a thrilling notion for Sci-Fi movies, AI is now an indisputable reality, and it has taken multiple industries by storm. From Large Language Models (LLMs) enabling fantastic chatbots like ChatGPT to Internet-of-Things (IoT) devices bringing about Industry 4.0, AI has found applications and use cases across almost every modern industry. While the majority of AI implementations have taken place centrally on cloud servers, there has been a strong pursuit for intelligence to be deployed locally at the edge.</p><p dir="ltr">Artificial intelligence at the edge is referred to as Edge AI, and it is the convergence of AI model deployment with edge computing. By bringing computational power closer to the data source, Edge AI offers real-time data processing on edge devices with reduced latency, higher bandwidth efficiency, enhanced reliability, and more robust security and privacy. This has the potential to propel AI capabilities beyond its current centralized, cloud-based structure. As the adoption of Edge AI continues to grow almost exponentially, we will witness a paradigm shift in how businesses deploy and utilize AI, especially with edge computing decentralizing data processing.</p><p dir="ltr">Industries that require real-time data processing capabilities are the primary industries in Edge AI’s scope of impact and are expected to experience a radical transformation as a result. Embracing this dramatic shift is essential for businesses that seek to stay ahead of the competition and leverage the benefits of real-time, context-aware decision-making. But knowing that Edge AI impacts every industry differently, how can a business in a specific industry embrace this technology to ensure it keeps pace with its rapid evolution?</p><p dir="ltr">Accordingly, Wevolver has partnered with several major companies and thought leaders in this space to produce this holistic report on the state of Edge AI in 2024 and its applications in every relevant industry, from healthcare to industrial IoT and manufacturing, smart cities, retail, energy, agriculture, and automotive. Each industry is covered by a dedicated chapter that provides industry-specific insights, descriptions, and examples, complemented by featured sections of real-world case studies. The report also provides a peek into the exciting generative AI technology and its convergence with edge computing before delving into the challenges still hindering Edge AI today. Finally, it gives a glimpse into the future of Edge AI and how it will develop in the years to come.</p><p dir="ltr">This is the second in-depth report on Edge AI by Wevolver after its <a href="https://www.wevolver.com/article/2023-edge-ai-technology-report" target="_blank">inaugural report in 2023</a>, which focused primarily on the technological side of Edge AI. This report explores Edge AI’s application side and its impact on the various industries mentioned above. Will Edge AI be one of the world’s fundamental technologies going forward? Will it continue to reshape our industries and become ubiquitous? If so, how? Let’s find the answers in the chapters below.</p><p>Samir Jaber, Report Editor-in-Chief</p><h2 dir="ltr">About the report</h2><p dir="ltr">This technology report came to fruition thanks to the collaborative efforts of multiple contributors. Samir Jaber, the editor-in-chief, alongside John Soldatos and Ravi Rao, the supporting authors, have played integral roles in researching, developing, and editing the report content. The insights provided by our sponsors and contributors have significantly enriched the content. Special acknowledgment goes to Jessica Miley, the content director at Wevolver, for her consistent support and leadership throughout the process. Each contributor’s dedication and input are deeply valued, highlighting the collective effort that has gone into creating this report.</p><h3 dir="ltr">About the authors</h3><p><strong>Samir Jaber</strong></p><p>Editor-in-Chief Leipzig, Germany&nbsp;</p><p>Samir Jaber is an editor, writer, and industry expert on technology, science, and engineering topics. He is an online content specialist with an academic background in mechanical engineering, nanotechnology, and scientific research. Samir has comprehensive experience working with major engineering and technology companies as a writer, editor, content manager, and digital marketing consultant. He is a featured author in 30+ industrial magazines with a focus on Artificial Intelligence (AI), the Internet of Things (IoT), 3D printing, Autonomous Vehicles (AV), nanotechnology, materials science, and sustainability. Samir is also an award-winning engineering researcher in the fields of nanofabrication and microfluidics.</p><p><strong>John Soldatos</strong></p><p>Co-author Athens, Greece&nbsp;</p><p>Honorary Research Fellow at the University of Glasgow John Soldatos holds a Ph.D. in Electrical &amp; Computer Engineering from the National Technical University of Athens (2000) and is currently an Honorary Research Fellow at the University of Glasgow, UK (2014-present). He was Associate Professor and Head of the Internet of Things (IoT) Group at the Athens Information Technology (AIT), Greece (2006–2019), and Adjunct Professor at the Carnegie Mellon University, Pittsburgh, PA (2007–2010). He has significant experience working closely with large multi-national industries (e.g., IBM, INTRACOM, INTRASOFT International) as an R&amp;D consultant and delivery specialist while being a scientific advisor to various high-tech startup enterprises. Dr. Soldatos is an expert in Internet-of- Things (IoT) and Artificial Intelligence (AI) technologies and applications, including IoT/AI applications in smart cities, finance (Finance 4.0), and industry (Industry 4.0).&nbsp;</p><p><strong>Ravi Rao</strong></p><p>Co-author Ahmedabad, India&nbsp;</p><p>Ravi is a software engineer at one of the world’s largest IT consulting firms. He works on digital transformation projects that help streamline operations for customers. Ravi’s education in Power Electronics Engineering and his professional experience have equipped him with expertise in electrical, electronics and computer engineering. Ravi has authored two IEEE research papers: one exploring the integration of AI and IoT in irrigation systems and another assessing the implementation of a curriculum to stimulate innovation among engineering students. Additionally, Ravi has earned a patent for a project aimed at enhancing independence for individuals with mobility challenges. Outside work, Ravi is passionate about writing on technology, gadgets, and gaming. He also loves engaging in conversations about the latest advancements in technology.</p><h2>About the Sponsors</h2><p>We want to acknowledge the support of our sponsors for their contribution to the State of Edge AI Report. The contributions provided were essential in facilitating the research and findings presented. Our sponsors' engagement reflects their commitment to the ongoing discussions and developments in the field of Edge AI.</p><p><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1712054291519-Mitsubishi+Chemical+Group++Growth+Garage++Open+Innovation+Program+%287%29.png" style="width: 100%px;" class="fr-fic fr-dib"></p><h2><strong>Download your copy of the 2024 State of Edge AI Report now.&nbsp;</strong></h2><p><span contenteditable="false" draggable="true" class="fr-video fr-deletable fr-fvc fr-dvb fr-draggable"><iframe width="540" height="450" src="https://c8056756.sibforms.com/serve/MUIFAIvLRE9FbYyICZVAlYl6oaSYA1y_4gbHWS82SbbrrHstSB6VMs2aj57Tvg0e16L_UZVprsukgYH2yB927lpe-0uPg3ZEAhJiDPRQb2_oMEFQLYiEcG6zXHV6DwzxZPaJo25g3EzCRsYZ5z3ey0dkbIZE_M-AQ1kRN3i2_dq-vjEv7FQC29p_-Vu8tD5vbyaerZZF9IYqTQ8D" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span></iframe></span></p><p dir="ltr">This is Wevolver's second in-depth report on Edge AI after its <a href="https://www.wevolver.com/article/2023-edge-ai-technology-report" target="_blank">inaugural report in 2023</a>, which focused primarily on the technological side of Edge AI.&nbsp;</p>

Introduction

<p dir="ltr" id="isPasted">We are pleased to share Chapter 1 of the&nbsp;<strong>2024 State of Edge AI Report</strong> with you.&nbsp;</p><p dir="ltr">This report represents months of hard work, research, and dedication to exploring the dynamic world of Edge AI applications across industries.</p><p dir="ltr"><strong>Report Highlights:</strong></p><ul><li dir="ltr"><p dir="ltr">#1:&nbsp;Industry-specific insights–&nbsp;Each industry is covered by a dedicated chapter that provides industry-specific insights, descriptions, and examples. ,</p></li><li dir="ltr"><p dir="ltr">#2: Real-life case studies – Get comprehensive insights from featured sections of real-world case studies.&nbsp;</p></li><li dir="ltr"><p dir="ltr">#3: A look into the future - The report also provides a peek into the exciting generative AI technology and its convergence with edge computing before delving into the challenges still hindering Edge AI today.</p></li></ul><p dir="ltr">Download the full report below.&nbsp;</p><p dir="ltr"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1712055006106-Mitsubishi+Chemical+Group++Growth+Garage++Open+Innovation+Program+%286%29.png" style="width: 100%px;" class="fr-fic fr-dib"></p><p dir="ltr"><span contenteditable="false" draggable="true" class="fr-video fr-deletable fr-fvc fr-dvb fr-draggable"><iframe width="540" height="505" src="https://c8056756.sibforms.com/serve/MUIFAIvLRE9FbYyICZVAlYl6oaSYA1y_4gbHWS82SbbrrHstSB6VMs2aj57Tvg0e16L_UZVprsukgYH2yB927lpe-0uPg3ZEAhJiDPRQb2_oMEFQLYiEcG6zXHV6DwzxZPaJo25g3EzCRsYZ5z3ey0dkbIZE_M-AQ1kRN3i2_dq-vjEv7FQC29p_-Vu8tD5vbyaerZZF9IYqTQ8D" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span></iframe></span></p><h2 dir="ltr">The Evolution of AI: From Research to Edge Technology</h2><p dir="ltr" id="isPasted">Edge AI’s growing momentum over recent years has not been a stroke of luck or an unexpected turn of events. It’s been brewing in the research spheres for quite a while now. In fact, the way AI influences technology has been a discussion since the mid-1900s, ever since Alan Turing set the standards for the “thinking machine,” and Christopher Strachey wrote the first successful AI computer program, followed by Arthur Samuel of IBM pioneering machine learning. From then on, AI went on a long rollercoaster ride of rises and dips, going through hype cycles and significant interest and funding all the way down to not one but two “AI winters” before the turn of the millennium. Nonetheless, in recent years, especially going into the 2020s, AI has taken yet another leap, but this time, it has taken off.</p><p dir="ltr">With major developments in machine learning and the advent of technologies like cloud computing, edge computing, and fog computing, AI has significantly benefited from the data processing capabilities brought about by these technologies. Cloud computing’s ability to process massive amounts of data simultaneously and edge computing’s ability to process data locally and in real time are both crucial for AI’s mass adoption. This helps AI make informed decisions in fractions of the time that it would take humans, thus improving processes and systems across different industries and creating new and optimal ways of working. As a result, AI has witnessed significant growth in adoption rate across various large organizations, reaching 42% in 2023, according to the <a href="https://noticias.ai/wp-content/uploads/2024/01/IBM-Global-AI-Adoption-Index-Report-Dec-2023.pdf" target="_blank" data-lf-fd-inspected-kn9eq4roawkarlvp="true">IBM Global AI Adoption Index 2023</a>, with as many as 40% of organizations actively exploring the use of AI in their business operations. AI has once again captured the spotlight — and this resurgence has brought about a new wave of possibilities and opportunities to explore.</p><blockquote><p dir="ltr" id="isPasted">“Simplicity is key in the tech world: a solution is only as successful as it is widely accepted, adopted, and applied. That’s why Arduino’s mission is to democratize technologies like Edge AI, making it an accessible option for people with different backgrounds and in all industries to solve problems, create value, and grow.” &nbsp;– Fabio Violante, CEO of Arduino</p></blockquote><p>Today, a lot of data processing is being decentralized from large cloud data centers to smaller localized data centers and edge devices. This has enabled the emergence of Edge AI, which processes data at or near the source of data generation. Many organizations are deploying edge functionalities, resulting in energy-efficient, low-latency applications with real-time performance. Edge AI offers significant data protection and security benefits, making it an attractive proposition for organizations across sectors to use edge computing features for various use cases. In the following sections, we take a look at the Edge AI market to see how it’s been responding to the technology’s potential, and we explore how different industries are adopting Edge AI into their workflows and systems.</p><h3 dir="ltr" id="isPasted">Edge AI Market Landscape</h3><p dir="ltr">For a technology that is moving quickly on the Gartner <a href="https://www.gartner.com/en/articles/what-s-new-in-artificial-intelligence-from-the-2023-gartner-hype-cycle" target="_blank">hype cycle for AI</a>, Edge AI is constantly finding new use cases in various industries, and its adoption is expected to grow further and faster. In fact, Gartner analysts predict that <a href="https://www.gartner.com/en/documents/4850031" target="_blank">edge computing technologies</a> will gain traction and maturity in 2024, especially with the significant drop in the cost of developing and deploying edge systems thanks to technical innovation in this space. Such improvements in the technology have enabled the Edge AI market to witness remarkable growth over recent years. According to <a href="https://market.us/report/edge-ai-market/" target="_blank">Market.US</a> research, the global Edge AI market is expected to surpass the USD 140 billion mark by 2032, a considerable rise from just over USD 19.1 billion in 2023. That is a compound annual growth rate (CAGR) of almost 26% across nine years.</p><p dir="ltr"><span class="fr-img-caption fr-fic fr-dib" style="width: 769px;"><span class="fr-img-wrap"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1710785750352-240303_IMAGES-01.jpg" style="width: 769px;" class="fr-fic fr-dib"><span class="fr-inner">The Edge AI Market is expected to reach USD 143.6 Billion by 2032, an exponential rise from its 2023 value of USD 19.1 Billion (Credit: market.us)</span></span></span></p><p dir="ltr">The growth of the Edge AI market reflects the increasing integration of technology into various aspects of modern life. With the proliferation of IoT devices across industries, from manufacturing to healthcare, vast volumes of data are being generated continuously. This data holds significant potential for insights and optimization, but traditional centralized processing methods often struggle to handle real-time data processing without having to deal with latency issues.</p><p dir="ltr">Edge AI addresses this challenge by bringing AI and machine learning algorithms closer to where the data is generated, at the “edge” of the network. This localized approach allows for real-time processing and analysis, minimizing latency, reducing bandwidth usage, and enabling quicker decision-making. For instance, within the realm of autonomous vehicles, split-second responses to changing road conditions are crucial for safety. Edge AI enables these vehicles to process sensor data onboard, ensuring faster reaction times.</p><p dir="ltr">Moreover, advancements in semiconductor technology have played a crucial role in enabling more powerful and energy-efficient edge computing devices. These devices are capable of handling complex AI algorithms while remaining efficient enough to operate in resource-constrained environments, such as remote industrial sites or within wearable devices.</p><p dir="ltr">The rollout of 5G technology further amplifies the capabilities of Edge AI solutions. With its significantly enhanced connectivity and data transfer speeds, 5G facilitates seamless communication between edge devices and central systems, enabling faster data transmission and response times. This is particularly beneficial in scenarios such as healthcare monitoring, where timely analysis of patient data can have life-saving implications.</p><p dir="ltr">In essence, the growth of the Edge AI market represents a response to the evolving demands of industries and engineers in a data-driven world. It's about leveraging innovation to optimize processes, enhance efficiency, and ultimately improve the way industries operate.</p><h3 dir="ltr" id="isPasted">Industry Adoption and Trends of Edge AI</h3><p dir="ltr">“Under the radar thus far, edge is set to become a ubiquitous lever of scale and reinvention as artificial intelligence (AI)—including generative AI—driven applications become pervasive in enterprise functions and operations.” This is what researchers at Accenture articulated in their 2023 study on <a href="https://www.accenture.com/content/dam/accenture/final/accenture-com/document-2/Accenture-Leading-With-Edge-Computing.pdf" target="_blank" data-lf-fd-inspected-kn9eq4roawkarlvp="true">Leading with Edge Computing</a>. This statement sums up the current state of Edge AI in a nutshell. With edge computing permeating a lot of industries and application areas, it is reaching a level of ubiquity that renders its implementation almost a necessity. In fact, Accenture’s survey uncovered that 83% of executives across multiple industries think that in order to stay competitive in the future, edge computing will be essential. And many are fearing missing out on all of Edge AI’s benefits if they do not act quickly and incorporate it into their workflows, products, and services.&nbsp;</p><p dir="ltr">However, the adoption of Edge AI is not uniform. While some companies regard edge as a key differentiator to bring AI into their core business, others still struggle with fully leveraging the technology’s benefits, mostly due to them considering it a standalone technology and using it in ad-hoc projects. Adopting edge computing strategically and integrating it with existing cloud strategies has shown the best outcomes: Advanced users of edge are four times more likely to achieve accelerated innovation, nine times more likely to increase efficiency, and seven times more likely to reduce costs (Accenture, 2023).</p><p dir="ltr"><span class="fr-img-caption fr-fic fr-dib" style="width: 769px;"><span class="fr-img-wrap"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1710785803768-240303_IMAGES-02.jpg" style="width: 100%px;" class="fr-fic fr-dib"><span class="fr-inner">Edge moves computing closer to users and devices at the edge of the network, where it is the closest possible to data sources (Credit: Accenture)</span></span></span></p><p dir="ltr" id="isPasted">Looking at Edge AI from the industry level, we see a similar distribution, with some industries already utilizing the technology almost ubiquitously and others still exploring its potential. The manufacturing industry seems to take the lion’s share of revenue with approximately 31% thanks to the integration of automation and real-time insights, capitalizing on Edge AI’s benefits in defect detection, reduced latency, real-time decision-making, cost efficiency, and data security. Following it is the automotive and transportation sector, especially with the recent ACES trends (autonomous driving, connectivity, electric vehicles, and shared mobility) leading the industry, all of which require Edge AI, albeit at varying levels. Furthermore, traffic management is seeing significant developments by integrating Edge AI into its sensors, cameras, and traffic management systems (TMS). While other industries like healthcare, retail, energy, and agriculture acknowledge the potential of Edge AI, they are yet to adopt it at the same level as manufacturing and automotive.</p><p dir="ltr">That being said, Edge AI is seemingly showing clear patterns and trends that are influencing the future of data science and machine learning (DSML) across many industries. As <a href="https://zeenea.com/3-ai-trends-identified-by-gartner-to-look-out-for-in-2024/" target="_blank">Gartner</a> outlined in 2023, one trend that is leading the way is Edge AI as a promise of responsiveness. Edge AI promises quicker decision-making by executing AI algorithms locally, bypassing the need for the Cloud or remote data center connections. This reduces latency and enhances system responsiveness.&nbsp;</p><p dir="ltr"><span class="fr-img-caption fr-fic fr-dib" style="width: 769px;"><span class="fr-img-wrap"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1710785850995-240303_IMAGES-03.jpg" style="width: 100%px;" class="fr-fic fr-dib"><span class="fr-inner">How organizations have been using AI in the past two years (Credit: IBM)</span></span></span></p><p dir="ltr">Converging AI and edge computing leads to more efficient and potentially energy-saving solutions. Gartner has forecasted that, by 2025, more than half of data analysis by deep neural networks will occur at the point of capture in an edge system, a significant increase from single-digit percentage points in 2021. This shows the significance of Edge AI in the years to come and how its implementation will continue to grow and penetrate various systems and workflows.</p><hr><p><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1712055077687-Mitsubishi+Chemical+Group++Growth+Garage++Open+Innovation+Program+%286%29.png" style="width: 100%px;" class="fr-fic fr-dib"></p><p><span contenteditable="false" draggable="true" class="fr-video fr-deletable fr-fvc fr-dvb fr-draggable"><iframe width="540" height="505" src="https://c8056756.sibforms.com/serve/MUIFAIvLRE9FbYyICZVAlYl6oaSYA1y_4gbHWS82SbbrrHstSB6VMs2aj57Tvg0e16L_UZVprsukgYH2yB927lpe-0uPg3ZEAhJiDPRQb2_oMEFQLYiEcG6zXHV6DwzxZPaJo25g3EzCRsYZ5z3ey0dkbIZE_M-AQ1kRN3i2_dq-vjEv7FQC29p_-Vu8tD5vbyaerZZF9IYqTQ8D" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe></span><br></p>

Edge AI Market Analysis and Trends

<p dir="ltr" id="isPasted">We are thrilled to share Chapter 2 of the&nbsp;<strong>2024 State of Edge AI Report</strong> with you.&nbsp;</p><p dir="ltr">This report represents months of hard work, research, and dedication to exploring the dynamic world of Edge AI applications across industries.</p><p dir="ltr"><strong>Report Highlights:</strong></p><ul><li dir="ltr"><p dir="ltr">#1:&nbsp;Industry-specific insights–&nbsp;Each industry is covered by a dedicated chapter that provides industry-specific insights, descriptions, and examples. ,</p></li><li dir="ltr"><p dir="ltr">#2: Real-life case studies – Get comprehensive insights from featured sections of real-world case studies.&nbsp;</p></li><li dir="ltr"><p dir="ltr">#3: A look into the future - The report also provides a peek into the exciting generative AI technology and its convergence with edge computing before delving into the challenges still hindering Edge AI today.</p></li></ul><p dir="ltr">Download the full report below.&nbsp;</p><p dir="ltr"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1712055225143-Mitsubishi+Chemical+Group++Growth+Garage++Open+Innovation+Program+%286%29.png" style="width: 100%px;" class="fr-fic fr-dib"></p><p dir="ltr"><span contenteditable="false" draggable="true" class="fr-video fr-deletable fr-fvc fr-dvb fr-draggable"><iframe width="540" height="505" src="https://c8056756.sibforms.com/serve/MUIFAIvLRE9FbYyICZVAlYl6oaSYA1y_4gbHWS82SbbrrHstSB6VMs2aj57Tvg0e16L_UZVprsukgYH2yB927lpe-0uPg3ZEAhJiDPRQb2_oMEFQLYiEcG6zXHV6DwzxZPaJo25g3EzCRsYZ5z3ey0dkbIZE_M-AQ1kRN3i2_dq-vjEv7FQC29p_-Vu8tD5vbyaerZZF9IYqTQ8D" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe></span><br></p><h2 dir="ltr">Healthcare and Medical Applications</h2><p dir="ltr" id="isPasted">In recent years, the emergence of Edge AI has played a significant role in the digital transformation of the healthcare sector. With the shift of AI capabilities closer to the source of data generation, Edge AI can enable real-time, data-driven clinical decisions, enhance accuracy, and boost privacy and data protection in various healthcare applications where sensitive data are used. There are a multitude of Edge AI use cases in medical and clinical settings, including use cases that improve existing capabilities and others that enable functionalities that were hardly possible before the advent of AI at the edge. These capabilities fulfill different objectives and serve the needs of various stakeholders, as outlined in the Edge AI use cases presented below.</p><h3 dir="ltr">Real-Time Patient Monitoring</h3><p dir="ltr">Continuous patient monitoring is crucial to ensuring timely detection of warning signs and providing appropriate interventions. Edge AI plays a vital role in patient monitoring use cases by enabling real-time analysis of data collected from various data sources like wearable devices, sensors, and electronic health records (EHR). Based on data processing at the edge, patient monitoring systems can instantly identify anomalies and trigger appropriate actions, such as alerting healthcare providers or autonomously adjusting medication dosages. Hence, real-time patient monitoring powered by Edge AI can significantly improve patient outcomes and reduce the risk of adverse events.</p><p dir="ltr"><span class="fr-img-caption fr-fic fr-dib" style="width: 769px;"><span class="fr-img-wrap"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1710785926339-4.jpg" style="width: 100%px;" class="fr-fic fr-dib"><span class="fr-inner">Edge AI is transforming the medical industry by enabling digital diagnosis and remote patient monitoring (Credit: Jenny Wang, Seeed Studio)</span></span></span></p><table style="width: 100%;"><tbody><tr><td style="width: 100%;"><p dir="ltr" id="isPasted">CASE STUDY: IMAGIMBOB</p><h2 id="isPasted">A Fast, Cost-Effective, and Reliable Way to Add Smart Features to Healthcare Devices</h2><p dir="ltr" id="isPasted">The healthcare industry has seemingly infinite vital signs and symptoms to track; finding ways to turn these signals into medically useful information can mean game-changing improvements for the industry and its patients. Edge AI offers an exciting opportunity to propel medical care and healthful living to an impactful new level. It is particularly suitable for the privacy requirements of this industry because data can be kept on the device instead of being sent to the cloud.</p><p dir="ltr">Among those testing the way forward is Imagimob, a company intent on making it easier to deploy machine learning on edge devices. With their collection of off-the-shelf models that can be added to healthcare and wearable products with ease and an end-to-end development platform for solving problems with custom Edge AI models, Imagimob’s tools can change healthcare for the better.</p><h2>The Fastest Way to Launch Smart AI Features</h2><p dir="ltr">A major barrier to creating smarter medical devices is the typically extensive development process. With Imagimob’s Ready Models, AI features can easily be deployed onto existing products without the significant time, cost, or machine learning expertise required for custom development.&nbsp;</p><p dir="ltr">Ready Models currently available for use in healthcare include audio-based models for coughing and snoring detection. And this is just the beginning — additional Ready Models under development include models based on Radar, IMU, and Capacitive Sensing, which can be used for presence detection, fall identification, and more. &nbsp;</p><h3>Developing Sharper Models in Real Time&nbsp;</h3><p dir="ltr">Imagimob’s machine learning development platform, Imagimob Studio, covers the entire workflow — from data collection to quick deployment in a healthcare product. Their recently launched Graph UX interface helps produce even higher quality models by letting engineers see them working in real-time.</p><p dir="ltr">Take, for instance, a model that identifies coughing in a healthcare setting. In a scenario where coughs are being under-identified, an engineer can pinpoint which data the model is failing to classify and make direct improvements.&nbsp;</p><p dir="ltr">“Graph UX makes models more robust as you have greater visibility and can identify problems fast,” says Alexander Samuelsson, Imagimob’s CTO. “This is a great advantage for the healthcare industry, where the ability to adapt quickly to new scenarios and information can be critical.”</p><p dir="ltr">The possibilities are limitless. Learn more about how Imagimob’s Edge AI solutions are helping to build the intelligent products of the future with applications in healthcare and beyond at <a href="http://www.imagimob.com" target="_blank">www.imagimob.com</a>.</p></td></tr></tbody></table><h2 dir="ltr">On-Device Medical Imaging and Smart Rehabilitation</h2><p dir="ltr">Traditional medical imaging techniques require transferring large amounts of data to remote servers for processing, which creates concerns about latency and privacy. With Edge AI, medical imaging can be performed directly on the device, such as ultrasound machines and MRI (Magnetic Resonance Imaging) scanners. This ensures enhanced accuracy and reduced diagnosis time while increasing data protection. In particular, Edge AI algorithms can detect abnormalities in scans and provide immediate feedback to radiologists, enabling more efficient diagnoses. Furthermore, Edge AI addresses privacy concerns by minimizing the transmission of sensitive patient data over the network.</p><p dir="ltr">Technology trends like TinyML, embedded machine learning, and on-device neuromorphic computing are likely to increase the number and variety of embedded medical imaging applications in the years to come. On-device inference for medical decision-making is set to become smaller yet faster, smarter, and more privacy-friendly.</p><p dir="ltr">Similarly, rehabilitation processes can benefit significantly from real-time feedback in order to optimize therapy routines and improve patient outcomes. Edge AI empowers smart rehabilitation devices with on-device inference, allowing them to analyze sensor data and provide real-time feedback to patients during rehabilitation sessions. For instance, an Edge-AI-powered prosthetic limb could adjust its movements based on the patient's gait analysis toward a more natural and personalized experience. Based on Edge AI, rehabilitation devices can adapt to individual needs in real time, which enhances the effectiveness of therapies and reduces the need for constant supervision.</p><h3 dir="ltr">Clinical Trials with Real-World Data</h3><p dir="ltr">Real-time medical adherence monitoring is essential in clinical trials to ensure accurate data collection and protocol compliance. Edge AI enables the integration of real-time monitoring devices that track medication intake, adherence to treatment plans, and patient vitals. By leveraging Edge AI algorithms, researchers can assess, in real time, whether participants are following the prescribed protocols.&nbsp;</p><p dir="ltr">Such functionalities can provide valuable insights into the effectiveness of treatments while reducing the burden of manual data collection. Most importantly, they are key to ensuring that clinical trials adhere to the prescribed protocols, which increases their credibility. Edge AI on real-world data is, therefore, a technology that will be increasingly adopted and used by Contract Research Organizations (CROs) in a variety of clinical trials worldwide.</p><h3 dir="ltr">Prediction, Detection, and Tracking of Disease Outbreaks</h3><p dir="ltr">Processing local data at the edge is particularly valuable for predicting and tracking disease outbreaks. Based on the analysis of medical records, wearables data, and environmental factors in real-time at the edge, healthcare organizations can detect early signs of potential outbreaks. This is fundamental to enabling proactive measures to prevent their spread. Edge AI can also ensure privacy and data protection by minimizing the reliance on centralized data repositories, which mitigates the risk of unauthorized access to sensitive healthcare information.</p><p dir="ltr">Many healthcare applications can also benefit from decentralized learning models at the edge, such as federated learning. Federated learning is a technique in which global models are developed by combining locally trained models. It is already used in various disease detection and prediction use cases to enhance accuracy. The latter stems from the fact that global models turn out to be more accurate than local models, as they are, in principle, trained on more data.&nbsp;</p><p dir="ltr">In the scope of a federated learning approach, edge devices (e.g., smartphones or IoT sensors) locally train AI models using data from individual patients or individual care service providers (e.g., care centers). Encrypted model updates are then sent to a central server, where they are combined to create a global model without compromising the privacy of individual data. This approach has been successfully applied by organizations like the World Health Organization (WHO) during the COVID-19 pandemic, where local models trained on edge devices contribute to a comprehensive global model that aided in disease detection and prediction.</p><p dir="ltr">Decentralized learning paradigms will be increasingly adopted as the number of connected medical devices and other healthcare data sources grows rapidly. Managing huge numbers of data sources in a centralized manner increases data protection risks and creates latency and power efficiency concerns. This is why it has to be avoided whenever possible and substituted by decentralized systems powered by Edge AI.</p><p dir="ltr"><span class="fr-img-caption fr-fic fr-dib" style="width: 769px;"><span class="fr-img-wrap"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1710786074822-240303_IMAGES-04.jpg" style="width: 100%px;" class="fr-fic fr-dib"><span class="fr-inner">Artificial intelligence-enabled remote patient monitoring architectures (Credit: Shaik, T. et al., WIREs)<br><br></span></span></span></p><p dir="ltr" id="isPasted">Overall, Edge AI applications in healthcare offer numerous benefits, ranging from real-time patient monitoring and on-device medical imaging to clinical trials with real-world data and smart rehabilitation devices. Based on Edge AI paradigms, healthcare organizations can achieve real-time performance, enhanced accuracy, and improved patient outcomes. Moreover, privacy and data protection are prioritized due to the reduced transmission of sensitive information over networks, minimizing the attack surface of the healthcare application. In the coming years, Edge AI technologies will empower healthcare professionals to work faster and more effectively, delivering tangible benefits to millions of patients worldwide.</p><p dir="ltr"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1712055297468-Mitsubishi+Chemical+Group++Growth+Garage++Open+Innovation+Program+%286%29.png" style="width: 100%px;" class="fr-fic fr-dib"></p><p><span contenteditable="false" draggable="true" class="fr-video fr-deletable fr-fvc fr-dvb fr-draggable"><iframe width="540" height="505" src="https://c8056756.sibforms.com/serve/MUIFAIvLRE9FbYyICZVAlYl6oaSYA1y_4gbHWS82SbbrrHstSB6VMs2aj57Tvg0e16L_UZVprsukgYH2yB927lpe-0uPg3ZEAhJiDPRQb2_oMEFQLYiEcG6zXHV6DwzxZPaJo25g3EzCRsYZ5z3ey0dkbIZE_M-AQ1kRN3i2_dq-vjEv7FQC29p_-Vu8tD5vbyaerZZF9IYqTQ8D" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe></span><br></p>

Healthcare and Medical Applications

Industrial IoT and Manufacturing

Smart Cities and Urban Infrastructure

<p dir="ltr" id="isPasted">We are pleased to share chapter 5 of the <strong>2024 State of Edge AI Report</strong> with you.&nbsp;</p><p dir="ltr">This report represents months of hard work, research, and dedication to exploring the dynamic world of Edge AI applications across industries.</p><p dir="ltr"><strong>Report Highlights:</strong></p><ul><li dir="ltr"><p dir="ltr">#1:&nbsp;Industry-specific insights–&nbsp;Each industry is covered by a dedicated chapter that provides industry-specific insights, descriptions, and examples. ,</p></li><li dir="ltr"><p dir="ltr">#2: Real-life case studies – Get comprehensive insights from featured sections of real-world case studies.&nbsp;</p></li><li dir="ltr"><p dir="ltr">#3: A look into the future - The report also provides a peek into the exciting generative AI technology and its convergence with edge computing before delving into the challenges still hindering Edge AI today.</p></li></ul><p dir="ltr">Download the full report below.&nbsp;</p><p dir="ltr"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1712058814314-Mitsubishi+Chemical+Group++Growth+Garage++Open+Innovation+Program+%286%29.png" style="width: 100%px;" class="fr-fic fr-dib"></p><p dir="ltr"><span contenteditable="false" draggable="true" class="fr-video fr-deletable fr-fvc fr-dvb fr-draggable"><iframe width="540" height="505" src="https://c8056756.sibforms.com/serve/MUIFAIvLRE9FbYyICZVAlYl6oaSYA1y_4gbHWS82SbbrrHstSB6VMs2aj57Tvg0e16L_UZVprsukgYH2yB927lpe-0uPg3ZEAhJiDPRQb2_oMEFQLYiEcG6zXHV6DwzxZPaJo25g3EzCRsYZ5z3ey0dkbIZE_M-AQ1kRN3i2_dq-vjEv7FQC29p_-Vu8tD5vbyaerZZF9IYqTQ8D" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe></span><br></p><h1 id="isPasted">Retail and Customer Experience</h1><p dir="ltr" id="isPasted">The retail sector is undergoing a significant transformation driven by Edge AI, responding to mounting margin pressures and evolving consumer expectations. According to a <a href="https://www.mckinsey.com/industries/retail/our-insights/automation-in-retail-an-executive-overview-for-getting-ready" target="_blank">report</a> by McKinsey &amp; Company, retailers, from grocery to specialty stores, face margin pressures ranging from 100 to 500 basis points, highlighting a critical need for efficiency gains.</p><p dir="ltr">Edge AI offers a solution to improve margins by 300 to 500 basis points through technologies like self-checkout systems and digital shelves. By leveraging the capabilities of Edge AI, retailers are not only addressing the immediate challenges of margin pressure but also getting ready for a future where technology-driven solutions redefine the shopping experience.</p><blockquote><p dir="ltr" id="isPasted">“Edge AI transforms retail from transformative to experiential, revolutionizing customer interaction. It’s not merely about selling products; it’s about crafting personalized journeys that engage shoppers, fostering loyalty and driving growth.” – Samir Jaber, Editor-in-Chief</p></blockquote><h3 dir="ltr" id="isPasted">Inventory Management Perfected</h3><p dir="ltr">Efficient inventory management is a balancing act of critical importance. Retailers are constantly grappling with the twin challenges of stockouts and overstock — each carrying its own set of repercussions.&nbsp;</p><p dir="ltr">Stockouts can result in lost sales and erode customer trust and satisfaction, leading to potential long-term implications on brand loyalty. Conversely, overstock situations tie up capital, increase storage costs, and lead to wastage, especially for perishable goods. These inventory misalignments directly impact a retailer's revenue and customer experience, making effective inventory management a foundation of successful retail operations.</p><p dir="ltr">In response to these challenges, the integration of Edge AI in inventory management has emerged as a transformative solution. For instance, smart shelves equipped with weight sensors and RFID tags can automatically monitor stock levels. These shelves relay real-time data to Edge AI systems, which then analyze and predict stock requirements, minimizing the occurrences of stockouts and overstock. Another example is the use of AI-powered cameras that track inventory movement, providing instant data on stock levels and customer preferences.</p><p dir="ltr"><span class="fr-img-caption fr-fic fr-dib" style="width: 769px;"><span class="fr-img-wrap"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1710789963040-14.jpg" style="width: 100%px;" class="fr-fic fr-dib"><span class="fr-inner">Shelves with digital displays for price tags (Credit: MediaTile)<br><br></span></span></span></p><p dir="ltr" id="isPasted">Edge AI also enables retailers to seamlessly integrate online and offline retail experiences. This technological advancement hinges on Edge AI's capability to provide real-time updates on inventory levels, ensuring accurate and current information is available to customers regardless of their shopping channel. Such real-time synchronization is essential in today's omni-channel retail landscape, where a smooth transition between online browsing and in-store purchasing is expected.&nbsp;</p><p dir="ltr">For instance, advanced predictive analytics and machine learning algorithms are employed to anticipate stock requirements, while digital price tags updated via Edge AI systems maintain price consistency across platforms. By utilizing these Edge AI-driven solutions, retailers effectively reduce the occurrence of stockouts and overstock, enhancing inventory management efficiency and ensuring a seamless and satisfying shopping journey for the customer.</p><table style="width: 100%;"><tbody><tr><td style="width: 100%;">CASE STUDY: SONY<h2>Scaling E-Commerce with Vision-Based AI for Inventory Management and Automation</h2><p dir="ltr">Retail and e-commerce center around customer satisfaction, reflected through online product reviews and ratings. While scaling retail or e-commerce, hidden pitfalls like inefficient warehouse utilization and inaccurate inventory affect their seller rating by customers, siphon profits, and stifle growth.</p><p dir="ltr">This poses a challenge for online retailers that sell the same goods on multiple websites. The importance of omnichannel for e-commerce means inventory needs to be accurately reflected across all sales channels, instantly. When it’s not, order cancellations or wrongly shipped items can decrease seller ratings, resulting in deprioritization of the vendor’s product listings, a loss of sales influence of up to 80%, or even vendor suspensions until issues are addressed.&nbsp;</p><p dir="ltr">The e-commerce boom brought FrioCONNECT challenges: inventory was inaccurate, counts were tedious, and 2% of listings were oversold or incorrectly shipped.</p><p dir="ltr">When 20% of items don’t have the potential for restocking, companies like FRIOCONNECT cover the sunk costs associated with the mistake. To manage inventory, the process of counting 100,000+ items and 5,000 SKUs took five employees 10 days to complete. Without the counts, overselling or sending wrong items became more frequent.</p><p dir="ltr">Gaining real-time, high-precision inventory visibility, once a luxury, was now a strategic imperative. FrioCONNECT recognized that automation of cycle counts was necessary to ensure correct inventory was represented on sales channels and to support the customer experience.</p><p dir="ltr">Partnering with system integrator AVALTON and Sony’s AITRIOS™ team, the PHOTOBAR solution was created, leveraging Sony’s AITRIOS platform and IMX500 intelligent image sensor. In 2022, FRIOCONNECT dispatched over 75,000 items and realized a net profit of nearly 5-8% per product. They determined, based on their 7,800 square foot Doylestown, PA warehouse, that 348 IMX500-equipped devices would be needed to cover 58 racks and 696 shelves.&nbsp;</p><p dir="ltr"><br></p><p dir="ltr"><span class="fr-img-caption fr-fic fr-dib" style="width: 751px;"><span class="fr-img-wrap"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1710790025273-15.jpg" style="width: 100%px;" class="fr-fic fr-dib"><span class="fr-inner">Sony’s vision-based AI for inventory management and automation (Credit: Sony)</span></span></span></p><p dir="ltr" id="isPasted">The solution automates cycle counting in real time, using edge devices to capture shelf images, process on-chip, and send only inventory-relevant data to an API for analysis and counting. Now, the 10-day-long process can be completed twice a day or as often as necessary.</p><p dir="ltr">Empowered by vision-based inventory management, FrioCONNECT increased order picking by 20%, optimized existing space, and is poised to gain back a projected $31,200 in error-based lost revenue from 2022, breaking even on their deployment of vision AI within a year, now with plans to expand the use of the technology across remaining warehouses.</p></td></tr></tbody></table><h3 dir="ltr" id="isPasted">Customer Behavior Analysis: Personalization at Scale</h3><p dir="ltr">In today's retail landscape, personalization is increasingly vital, with customers seeking experiences that cater specifically to their preferences and behaviors. Edge AI is instrumental in this shift, enabling the collection and analysis of customer data in real time. Brands leverage AI engines to parse through data gathered from online browsing, past purchases, and in-store interactions, which allows for tailored product recommendations.&nbsp;</p><p dir="ltr">This use of data creates personalized shopping experiences and targeted marketing strategies, enabling retailers to meet the growing consumer demand for personalization with greater precision and effectiveness.</p><p dir="ltr">Beyond online personalization, Edge AI is transforming the in-store experience. Through the integration of interactive displays and the analysis of data from in-store customer interactions, Edge AI aids retailers in comprehensively understanding and catering to consumer preferences.</p><p dir="ltr"><span class="fr-img-caption fr-fic fr-dib" style="width: 769px;"><span class="fr-img-wrap"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1710790099638-16.jpg" style="width: 100%px;" class="fr-fic fr-dib"><span class="fr-inner">Smart carts can transform the shopping experience by providing customers with product recommendations, navigating them through the store, and facilitating seamless checkout processes (Credit: Caper).<br><br></span></span></span></p><p dir="ltr" id="isPasted">Retailers can employ smart cameras and sensors within the store to track and analyze customer movements and their interactions with products in real time. This rich stream of data informs decisions on product placement, store layout, and marketing strategies. By leveraging these insights, shops are empowered to optimize the environment, making it more engaging, tailored, and responsive to the unique behaviors and preferences of their customers.</p><p dir="ltr">Implementing Edge AI in customer behavior analysis presents mutual benefits for retailers and consumers. For retailers, the enhanced understanding of customer preferences leads to increased sales and better inventory management. Consumers, on the other hand, enjoy a more personalized shopping experience with recommendations and offers that are relevant to their interests and needs. This synergy is often supported by industry data and case studies, showcasing significant improvements in customer satisfaction and loyalty.</p><table style="width: 100%;"><tbody><tr><td style="width: 100%;">CASE STUDY: QUALCOMM<h2>Qualcomm Technologies: The Power to Transform Retail with On-Device Generative AI</h2><p dir="ltr">On-device AI fuels a more capable, cost-efficient, reliable, private, secure, and promising path forward for retail. Capable of working in harmony with cloud AI, edge devices deliver a faster, more efficient, and highly optimized AI with computing power you can rely on.</p><p dir="ltr">Qualcomm technologies are helping retailers stay competitive in the digital age by transforming retail with on-device, generative AI. Last year, the breakthroughs in generative AI were monumentally exciting. Aware of its vast potential for several years, Qualcomm Technologies, Inc. has made it so that AI is holistically engineered into our processors. This year, in-store and online shopping are expected to remain equally popular. As physical retail regains momentum, we see an opportunity to fulfill consumers’ expectations by enabling seamless and integrated in-store experiences.</p><p dir="ltr">From on-device Generative AI applications for front-line workers and retail analytics to biometric Point-of-Sales (POS) payments and frictionless self-checkout and loss prevention, Qualcomm processors power many commercially available retail solutions and help deliver real results through:&nbsp;</p><ul><li dir="ltr"><p dir="ltr">Generating insights for retailers’ decision-making</p></li><li dir="ltr"><p dir="ltr">Boosting loss prevention to address the industry’s $112B “<a href="https://cdn.nrf.com/sites/default/files/2023-09/NRF_National_Retail_Security_Survey_2023.pdf" target="_blank" data-lf-fd-inspected-kn9eq4roawkarlvp="true">retail shrink</a>” problem</p></li><li dir="ltr"><p dir="ltr">Automating manual tasks to augment the workforce and minimize error rates</p></li><li dir="ltr"><p dir="ltr">Enhancing overall customer experience as consumers increasingly return to in-person shopping</p></li></ul><p dir="ltr">Our ecosystem of top-notch customers and collaborators is making these transformations a reality. Their products address many of the primary challenges retailers face in areas such as frictionless checkout, loss prevention, inventory management, and operational analytics while also enhancing the all-around shopper’s journey. <a href="https://www.qualcomm.com/news/onq/2024/01/the-power-to-transform-retail-with-on-device-generative-ai" target="_blank">Learn more</a> about how Qualcomm technologies are transforming retail in 2024 and explore further in <a href="https://www.qualcomm.com/content/dam/qcomm-martech/dm-assets/documents/qualcomm-demonstrations-from-nfr-2024-event.pdf" target="_blank" data-lf-fd-inspected-kn9eq4roawkarlvp="true">this infographic</a>.</p>For more information, check out <a href="https://www.qualcomm.com/products/internet-of-things/industrial/retail" target="_blank">Qualcomm Technologies’ retail solutions</a>.&nbsp;</td></tr></tbody></table><h3 dir="ltr" id="isPasted">Checkout Automation for Reduced Waiting Times</h3><p dir="ltr">One of the perennial pain points in the retail experience is the checkout process. Traditional checkout methods are often plagued with long queues and transactional delays, detracting from customer satisfaction and efficiency. These inefficiencies not only frustrate customers but also impact the overall throughput of retail operations — Edge AI steps in as a game-changer, offering innovative solutions to streamline the checkout process.</p><p dir="ltr">Examples of such innovations include automated checkout systems and smart carts. Automated checkout systems, which use computer vision and sensor fusion, are capable of identifying items in a cart and processing transactions without the need for manual scanning. This approach significantly speeds up the checkout process, enhancing customer satisfaction by reducing wait times. Smart carts, on the other hand, are equipped with barcode scanners and payment systems that allow customers to scan and pay for items as they shop, further reducing queue times.</p><p dir="ltr">Another application of Edge AI in retail is through self-checkout kiosks. These kiosks utilize machine learning algorithms to quickly identify items, offer various payment options, and even provide personalized offers based on the customer's shopping history. This not only enhances the checkout experience but also reduces wait times, thereby improving overall customer satisfaction and efficiency in retail operations.</p><p dir="ltr">By minimizing the time spent in queues and making transactions more seamless, retailers can ensure a more positive end-to-end shopping experience for their customers. Moreover, these technologies often lead to better resource allocation within stores, as employees freed from traditional checkout roles can focus on customer service and other value-added activities.</p><p dir="ltr"><span class="fr-img-caption fr-fic fr-dib" style="width: 769px;"><span class="fr-img-wrap"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1710790956909-17+small.jpg" style="width: 100%px;" class="fr-fic fr-dib"><span class="fr-inner">In addition to security and surveillance, cameras can play a crucial role in gathering valuable insights into customer behavior, enabling personalized shopping experiences.</span></span></span></p><p>The integration of specific Edge AI devices and technologies in retail is not just a futuristic concept but a present reality. From smart shelves to checkout automation, these innovations are reshaping the retail landscape, offering high levels of efficiency, personalization, and customer satisfaction like never before. As we look ahead, the continued advancement and integration of these technologies promise to revolutionize the retail experience further, making it more adaptive, responsive, and customer-centric.</p><p><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1712058858679-Mitsubishi+Chemical+Group++Growth+Garage++Open+Innovation+Program+%286%29.png" style="width: 100%px;" class="fr-fic fr-dib"></p><p><span contenteditable="false" draggable="true" class="fr-video fr-deletable fr-fvc fr-dvb fr-draggable"><iframe width="540" height="505" src="https://c8056756.sibforms.com/serve/MUIFAIvLRE9FbYyICZVAlYl6oaSYA1y_4gbHWS82SbbrrHstSB6VMs2aj57Tvg0e16L_UZVprsukgYH2yB927lpe-0uPg3ZEAhJiDPRQb2_oMEFQLYiEcG6zXHV6DwzxZPaJo25g3EzCRsYZ5z3ey0dkbIZE_M-AQ1kRN3i2_dq-vjEv7FQC29p_-Vu8tD5vbyaerZZF9IYqTQ8D" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe></span><br></p>

Retail and Customer Experience

<p dir="ltr" id="isPasted">We are pleased to share chapter 6 of the <strong>2024 State of Edge AI Report</strong> with you.&nbsp;</p><p dir="ltr">This report represents months of hard work, research, and dedication to exploring the dynamic world of Edge AI applications across industries.</p><p dir="ltr"><strong>Report Highlights:</strong></p><ul><li dir="ltr"><p dir="ltr">#1:&nbsp;Industry-specific insights–&nbsp;Each industry is covered by a dedicated chapter that provides industry-specific insights, descriptions, and examples. ,</p></li><li dir="ltr"><p dir="ltr">#2: Real-life case studies – Get comprehensive insights from featured sections of real-world case studies.&nbsp;</p></li><li dir="ltr"><p dir="ltr">#3: A look into the future - The report also provides a peek into the exciting generative AI technology and its convergence with edge computing before delving into the challenges still hindering Edge AI today.</p></li></ul><p dir="ltr">Download the full report below.&nbsp;</p><p dir="ltr"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1712058922621-Mitsubishi+Chemical+Group++Growth+Garage++Open+Innovation+Program+%286%29.png" style="width: 100%px;" class="fr-fic fr-dib"></p><p><span contenteditable="false" draggable="true" class="fr-video fr-deletable fr-fvc fr-dvb fr-draggable"><iframe width="540" height="505" src="https://c8056756.sibforms.com/serve/MUIFAIvLRE9FbYyICZVAlYl6oaSYA1y_4gbHWS82SbbrrHstSB6VMs2aj57Tvg0e16L_UZVprsukgYH2yB927lpe-0uPg3ZEAhJiDPRQb2_oMEFQLYiEcG6zXHV6DwzxZPaJo25g3EzCRsYZ5z3ey0dkbIZE_M-AQ1kRN3i2_dq-vjEv7FQC29p_-Vu8tD5vbyaerZZF9IYqTQ8D" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe></span><br></p><p id="isPasted">The global demand for energy is at an all-time high, driven by population growth and economic development. This relentless consumption exerts immense strain on our natural resources and contributes significantly to climate change. In response, improving energy efficiency and sustainability has become an absolute necessity.</p><p>Initiatives such as COP28 and the global stocktake that took place in November 2023 highlight this urgency, establishing ambitious benchmarks for international efforts. The search for solutions must extend beyond traditional methodologies, necessitating innovative approaches.</p><p>A notable example of such innovation is highlighted in a McKinsey report, which suggests that adopting AI-driven methods in grids could enhance energy efficiency by 2-5% at the very point of generation, with up to 10% improvement in production and 30% in cost savings. This shift towards Edge AI in areas such as energy consumption monitoring, renewable energy integration, and smart grid management marks the next logical step in harmonizing our energy needs with the planet's health.</p><blockquote><p>“Edge AI is a crucial technology in this world of finite resources. For example, it allows us to monitor and optimize electricity and water consumption in real time. Manufacturing, agriculture and logistics can thus minimize their impact, leading to huge cost savings and lowering the carbon footprint.” – Fabio Violante, CEO of Arduino</p></blockquote><h2>Smart Energy Monitoring for Consumer Awareness and Cost Savings</h2><p>Smart metering, bolstered by Edge AI, signifies a significant advancement in the way energy consumption is monitored. This combination not only facilitates real-time monitoring but also propels consumer engagement to new heights.&nbsp;</p><p><span class="fr-img-caption fr-fic fr-dib" style="width: 769px;"><span class="fr-img-wrap"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1710791714038-18+small.jpg" style="width: 100%px;" class="fr-fic fr-dib"><span class="fr-inner">Edge-AI-powered smart energy meters revolutionize energy management by providing precise, real-time data on consumption.</span></span></span></p><p>By providing detailed insights into energy usage patterns, consumers are empowered to make informed decisions, leading to more sustainable consumption habits. Edge AI plays a crucial role in this ecosystem by processing data on the spot, which helps quickly identify areas of excessive use or inefficiency.</p><p>Building on enhanced consumer awareness, Edge AI extends its impact to the operational level, driving efficiency and sustainability in various sectors. Implementing Edge AI in energy management systems has proven to be a game-changer for cost savings. In sectors such as commercial real estate, Edge AI has been instrumental in optimizing (HVAC systems, resulting in significant energy and cost savings.</p><p>Manufacturing is another area where Edge AI has made a substantial impact, streamlining energy consumption in line with production needs and thus lowering operational costs. These instances exemplify how Edge AI can foster consumer engagement and effectively reduce energy expenditure, aligning economic benefits with sustainability objectives.</p><h2>Innovations in Renewable Energy Integration Powered by Edge AI</h2><p>Renewable energy sources, such as solar and wind, are at the core of our efforts for a sustainable future. Edge AI enhances the integration of these renewable sources by optimizing their operation and efficiency. For solar energy systems, Edge AI algorithms adjust panel angles in real time to capture maximum sunlight. At the same time, such algorithms can optimize the blade rotation of wind turbines to harness wind energy more efficiently. The wind power generation maintenance market has also expanded, reaching approximately 35.7 billion RMB in China in 2022.&nbsp;</p><p>Utilizing AI visual inspection, manufacturers have developed systems that ensure over 95% accuracy in detecting critical issues like ice accretion and cracks, significantly reducing maintenance costs and enhancing turbine efficiency. By ensuring more reliable and efficient wind power generation, these Edge AI systems support the integration of wind energy into microgrids, which are essential for decentralized energy systems. This not only contributes to the resilience and sustainability of energy supply but also accelerates the transition towards a more diversified and renewable energy mix, aligning with global environmental and energy security goals. This real-time data processing and decision-making capability of Edge AI ensures that renewable energy sources operate at their peak efficiency, significantly increasing their output and reliability.</p><p>As Edge AI continues to refine the efficiency and output of renewable sources, it sets the stage for their increased adoption and integration into global energy systems. In countries leading in renewable energy, such as Germany and Denmark, Edge AI has played a pivotal role in managing the variability of renewable sources, ensuring a stable energy supply to the grid.</p><p><span class="fr-img-caption fr-fic fr-dib" style="width: 769px;"><span class="fr-img-wrap"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1710791616131-19+small.jpg" style="width: 100%px;" class="fr-fic fr-dib"><span class="fr-inner">Integrating renewable energy poses challenges due to its variability and the need for advanced grid management to ensure reliability and stability.</span></span></span></p><p>Similarly, in remote and emerging regions, Edge AI has facilitated the deployment of microgrids powered by renewable sources, providing reliable energy access to communities. &nbsp;The collaboration between Engie Energy Access and Atlas AI in Kenya serves as a compelling example of how AI can facilitate the deployment of microgrids powered by renewable sources, thus providing reliable energy access to remote and emerging regions. By leveraging Atlas AI's predictive analytics and high-resolution geospatial data, Engie was able to identify high-density areas with significant demand potential for off-grid solar solutions. This data-driven approach enabled Engie to target and expand their renewable energy solutions specifically to communities most in need, resulting in a 48% increase in monthly sales in a pilot program. The use of AI in this context not only optimized Engie's operational focus and product offerings but also significantly contributed to enhancing energy access in underserved areas. These examples underline the global applicability of Edge AI in enhancing the scalability and effectiveness of renewable energy, marking a step forward in the global pursuit of sustainability.</p><h2>Proactive Smart Grid Management through Edge AI Solutions</h2><p>Smart grid management represents a key area where Edge AI is making a significant impact, particularly in balancing supply and demand with greater precision. By leveraging real-time data analysis, Edge AI enables the grid to respond dynamically to changes in energy demand and supply conditions. This not only improves the reliability of the energy supply but also enhances the overall efficiency of the grid. For instance, during times of low demand, Edge AI can facilitate the storage of excess energy or manage its redistribution, optimizing grid operations.</p><p>Another area where Edge AI demonstrates its potential is in predictive maintenance, where its capabilities let it identify potential system vulnerabilities before they escalate. This foresight allows for preemptive adjustments to the grid, improving its resilience against disruptions and ensuring a stable energy supply. Predictive maintenance, enabled by Edge AI, can significantly reduce downtime and operational costs, further enhancing grid reliability and efficiency.</p><p>The discussion on smart grids just can't be completed without addressing the crucial integration and enhancement of electric vehicle (EV) charging infrastructure. The widespread adoption of EVs hinges heavily on a robust and intelligent charging network. In 2023, the European Commission passed a law mandating the expansion of the EV charging infrastructure to match the growth in EV adoption. For every battery-electric car registered in a member state, public charging stations must offer a power output of 1.3 kW. Furthermore, starting in 2025, there will be a requirement to install fast recharging stations with a minimum power of 150 kW every 60 km along the trans-European transport network (TEN-T), ensuring widespread and efficient charging options for EV users. Taking a look at another initiative, the government of India is focusing on improving the EV charging infrastructure in its capital. The plan involves setting up at least one charging station every 3 square kilometers, supporting the wider goal of electric vehicles making up 25 percent of all new vehicle registrations by the end of this year.&nbsp;</p><p>In this regard, Edge AI presents a game-changing opportunity to revolutionize this very infrastructure. Imagine charging stations that predict peak usage, dynamically allocate power based on vehicle needs, and even integrate with renewable energy sources for a truly sustainable experience. Through AI algorithms crunching real-time data on station availability, battery health, and even weather patterns, waiting times can plummet while charging efficiency skyrockets.</p><p><span class="fr-img-caption fr-fic fr-dib" style="width: 769px;"><span class="fr-img-wrap"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1710791660174-20.jpg" style="width: 100%px;" class="fr-fic fr-dib"><span class="fr-inner">Optimizing EV charging infrastructure with Edge AI is crucial for balancing grid demands and facilitating the transition to electric mobility (Credit: Ather Energy)</span></span></span></p><p>Predictive maintenance becomes possible, preventing downtime and ensuring smooth operation. Furthermore, edge AI empowers drivers with features like personalized charging plans and real-time updates on station status, fostering a seamless and stress-free journey. This revolution extends beyond individual stations, optimizing grid stability by intelligently managing power demands across entire networks. By harnessing the power of edge AI, we can unlock a future where EV charging is not just convenient but efficient, sustainable, and personalized, truly paving the way for a cleaner and more electrified tomorrow.</p><h2>Transforming the Conventional Energy Sector with Edge AI</h2><p>Edge AI is redefining safety and efficiency in the conventional energy sector, as well. On drilling floors, Edge AI-equipped cameras enhance safety by monitoring hazardous conditions in real time, significantly reducing the risk of accidents. Leak detection, traditionally reliant on extensive sensor networks, is revolutionized through Edge AI, where a single camera can identify leaks and other anomalies, streamlining monitoring processes and reducing equipment needs. Furthermore, in transportation and refinery operations, Edge AI optimizes routes and processes, improving efficiency and reducing environmental impact.&nbsp;</p><p>Illustrating the impact of Edge AI, Aramco, a leading hydrocarbon producer, has markedly improved its efficiency by adopting the technology. In its Khurais oil field, thousands of IoT sensors have enhanced oil well monitoring, cut power consumption by 18%, and reduced maintenance costs by 30%. The deployment of drones and wearable tech has also slashed inspection times by up to 90%. These applications of Edge AI not only promote operational safety and efficiency but also support the sector's shift towards more sustainable practices, highlighting the technology's potential to transform conventional energy production and management.</p><p>Edge AI's transformative impact on the energy sector is undeniable. Beyond smart metering and cost reduction, it optimizes renewable integration and enhances grid efficiency. While challenges persist, Edge AI represents a pivotal shift towards a sustainable future. Through responsible development and collaborative efforts, we can harness its power to unlock a cleaner, greener, and more resilient energy ecosystem, where sustainability is not a dream but a tangible reality.</p><p><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1712058953000-Mitsubishi+Chemical+Group++Growth+Garage++Open+Innovation+Program+%286%29.png" style="width: 100%px;" class="fr-fic fr-dib"></p><p><span contenteditable="false" draggable="true" class="fr-video fr-deletable fr-fvc fr-dvb fr-draggable"><iframe width="540" height="505" src="https://c8056756.sibforms.com/serve/MUIFAIvLRE9FbYyICZVAlYl6oaSYA1y_4gbHWS82SbbrrHstSB6VMs2aj57Tvg0e16L_UZVprsukgYH2yB927lpe-0uPg3ZEAhJiDPRQb2_oMEFQLYiEcG6zXHV6DwzxZPaJo25g3EzCRsYZ5z3ey0dkbIZE_M-AQ1kRN3i2_dq-vjEv7FQC29p_-Vu8tD5vbyaerZZF9IYqTQ8D" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe></span><br></p><p><br></p>

Energy Efficiency and Sustainability

<p dir="ltr" id="isPasted">We are pleased to share chapter 7 the <strong>2024 State of Edge AI Report</strong> with you.&nbsp;</p><p dir="ltr">This report represents months of hard work, research, and dedication to exploring the dynamic world of Edge AI applications across industries.</p><p dir="ltr"><strong>Report Highlights:</strong></p><ul><li dir="ltr"><p dir="ltr">#1:&nbsp;Industry-specific insights–&nbsp;Each industry is covered by a dedicated chapter that provides industry-specific insights, descriptions, and examples. ,</p></li><li dir="ltr"><p dir="ltr">#2: Real-life case studies – Get comprehensive insights from featured sections of real-world case studies.&nbsp;</p></li><li dir="ltr"><p dir="ltr">#3: A look into the future - The report also provides a peek into the exciting generative AI technology and its convergence with edge computing before delving into the challenges still hindering Edge AI today.</p></li></ul><p dir="ltr">Download the full report below.&nbsp;</p><p dir="ltr"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1712059017322-Mitsubishi+Chemical+Group++Growth+Garage++Open+Innovation+Program+%286%29.png" style="width: 100%px;" class="fr-fic fr-dib"></p><p dir="ltr"><span contenteditable="false" draggable="true" class="fr-video fr-deletable fr-fvc fr-dvb fr-draggable"><iframe width="540" height="505" src="https://c8056756.sibforms.com/serve/MUIFAIvLRE9FbYyICZVAlYl6oaSYA1y_4gbHWS82SbbrrHstSB6VMs2aj57Tvg0e16L_UZVprsukgYH2yB927lpe-0uPg3ZEAhJiDPRQb2_oMEFQLYiEcG6zXHV6DwzxZPaJo25g3EzCRsYZ5z3ey0dkbIZE_M-AQ1kRN3i2_dq-vjEv7FQC29p_-Vu8tD5vbyaerZZF9IYqTQ8D" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe></span><br></p><h2 dir="ltr"><strong>Agriculture and Food Production</strong></h2><p dir="ltr" id="isPasted">As the global population, <a href="https://ieeexplore.ieee.org/document/9505674/" target="_blank">projected</a> to surge to 8.5 billion by 2030 and 9.9 billion by 2050, demands more food, the agricultural sector faces the dual challenges of increasing production sustainably and ensuring food security. With the dire need to significantly ramp up production to meet this escalating demand, the adoption of Edge AI becomes not just advantageous but essential.&nbsp;</p><p dir="ltr">Edge AI is central to transforming food production, enabling advancements in precise management of crop/livestock, efficient resource utilization, enhanced quality assurance and beyond, addressing a spectrum of challenges with innovative solutions.</p><blockquote><p dir="ltr" id="isPasted">“Integrating Edge AI into agriculture is about leveraging technology to optimize resources, maximize yields, and ensure food security for a growing population.” – Samir Jaber, Editor-in-Chief</p></blockquote><h3 dir="ltr" id="isPasted">Improved Crop Monitoring and Analytics for Maximizing Yield</h3><p dir="ltr">Edge AI enhances agricultural productivity by enabling precise monitoring of crop health, growth patterns, and soil conditions. By processing data from an array of sensors—such as moisture, pH, and nutrient sensors — and high-resolution imaging technologies, Edge AI algorithms can identify early indicators of stress, disease, or nutrient imbalances in crops. This timely intervention allows for tailored management practices, such as targeted application of fertilizers or pesticides, leading to healthier crops and maximized yields.</p><p dir="ltr">Edge-AI-based systems can go a step further by harnessing real-time data on soil moisture and nutrient levels to optimize irrigation schedules and nutrient application, ensuring crops receive precisely what they need for optimal growth. This targeted approach not only conserves valuable resources but is also crucial in drought-prone areas, potentially turning the tide between crop failure and a successful harvest.</p><p dir="ltr"><span class="fr-img-caption fr-fic fr-dib" style="width: 769px;"><span class="fr-img-wrap"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1710791847903-21.jpg" style="width: 100%px;" class="fr-fic fr-dib"><span class="fr-inner">. Edge-AI-powered drones are transforming agriculture by enabling precise aerial monitoring and data collection, significantly improving crop health analysis and the efficiency of resource application (Credit: CropWatch).</span></span></span></p><p dir="ltr" id="isPasted">Furthermore, Edge AI empowers precision farming to become more controlled and accurate. Utilizing data-driven strategies, such as targeted irrigation and fertilization tailored to the unique needs of each plant, Edge AI significantly improves crop performance while reducing environmental footprints. <a href="https://ieeexplore.ieee.org/document/8453327" target="_blank">Variable rate technology (VRT)</a>, for instance, applies water, fertilizers, and pesticides at the right moment and location, maximizing efficiency and minimizing waste. Together, these Edge AI-driven practices represent a leap forward in sustainable agriculture, combining resource conservation with enhanced crop yields.</p><p dir="ltr">Edge AI also plays a pivotal role in promoting sustainable agriculture by enabling practices that conserve resources and reduce chemical usage. By providing detailed insights into crop and soil health, it helps in implementing conservation tillage, cover cropping, and integrated pest management strategies more effectively. This not only supports the health of the ecosystem but also ensures long-term agricultural productivity and food security.</p><p dir="ltr">Edge AI's applications extend beyond immediate farm-level benefits, laying a foundation for broader impacts on climate adaptation, economic sustainability, and informed policy-making. It is instrumental in adapting agricultural practices to changing climatic conditions and managing risks effectively. It also plays a crucial role in biodiversity conservation, monitoring ecosystem health, and promoting practices that sustain biodiversity within agricultural landscapes. The insights garnered from Edge AI applications can empower farmers with improved economic sustainability and influence agricultural policy-making, ensuring practices that are both productive and sustainable.</p><h3 dir="ltr">Streamlining Livestock Management with Edge AI</h3><p dir="ltr">Edge AI introduces a new era in livestock management, focusing on enhancing animal welfare while optimizing productivity. By employing real-time monitoring technologies, Edge AI systems track the health, behavior, and nutritional status of livestock, enabling early detection and treatment of illnesses, stress, or dietary deficiencies. This proactive approach not only improves the welfare of animals but also contributes to more efficient farming operations.</p><p dir="ltr"><span class="fr-img-caption fr-fic fr-dib" style="width: 769px;"><span class="fr-img-wrap"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1710791879865-22.jpg" style="width: 100%px;" class="fr-fic fr-dib"><span class="fr-inner">Edge AI and IoT can come together and enhance herd management, enabling real-time tracking of health and behavior. (Credit: Nvidia)</span></span></span></p><p dir="ltr" id="isPasted">From facial recognition for individual animal identification to automated systems for tailored nutrition and health management, Edge AI is transforming livestock care with precision and personalization. These technologies enable monitoring and management on a per-animal basis, improving the efficiency of breeding programs, and enhancing meat and milk quality. By leveraging Edge AI, farmers can make informed decisions that boost productivity while adhering to high welfare standards, showcasing a future where technology and traditional farming converge for superior outcomes.</p><h3 dir="ltr">Food Quality Assurance</h3><p dir="ltr">Edge AI plays a critical role in minimizing food waste and mitigating contamination risks throughout the supply chain. By employing advanced imaging and sensor technologies, Edge AI systems can detect early signs of spoilage or contamination in food products, allowing for immediate corrective actions.&nbsp;</p><p dir="ltr">This capability is instrumental in ensuring that food storage conditions are optimized, significantly extending shelf life and reducing waste. Additionally, Edge AI aids in the identification of potential contaminants before products reach consumers, enhancing food safety and quality.</p><p dir="ltr">The application of Edge AI in food production can go beyond waste reduction, significantly contributing to higher food safety standards. Through real-time monitoring and analysis, Edge AI can enable the detection of pathogens, toxins, and other harmful substances at various stages of the food supply chain.&nbsp;</p><p dir="ltr">This proactive approach to food safety can not only help in preventing health risks but also boost consumer confidence in food products. Edge AI-driven systems can facilitate compliance with stringent food safety regulations, ensuring that products meet all necessary standards before distribution.</p><p dir="ltr">Edge AI also streamlines supply chain operations, from demand forecasting to inventory management, ensuring the efficient delivery of fresh products. This operational efficiency is instrumental in reducing food waste and operational expenses. Furthermore, Edge AI fosters a transparent food system, where consumers gain insights into the food production journey, bolstering confidence and engagement.</p><p dir="ltr">Edge AI is at the forefront of today’s agricultural revolution, driving advancements in crop monitoring, livestock management, and food quality assurance. Its ability to process and analyze data in real time is transforming traditional farming practices, making agriculture more efficient, sustainable, and productive. As we look to the future, the continuous evolution of Edge AI holds the promise of further innovations, ensuring food security and environmental conservation for generations to come.</p><p dir="ltr"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1712059065584-Mitsubishi+Chemical+Group++Growth+Garage++Open+Innovation+Program+%286%29.png" style="width: 100%px;" class="fr-fic fr-dib"></p><p><span contenteditable="false" draggable="true" class="fr-video fr-deletable fr-fvc fr-dvb fr-draggable"><iframe width="540" height="505" src="https://c8056756.sibforms.com/serve/MUIFAIvLRE9FbYyICZVAlYl6oaSYA1y_4gbHWS82SbbrrHstSB6VMs2aj57Tvg0e16L_UZVprsukgYH2yB927lpe-0uPg3ZEAhJiDPRQb2_oMEFQLYiEcG6zXHV6DwzxZPaJo25g3EzCRsYZ5z3ey0dkbIZE_M-AQ1kRN3i2_dq-vjEv7FQC29p_-Vu8tD5vbyaerZZF9IYqTQ8D" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe></span><br></p>

Agriculture and Food Production 

<h3 dir="ltr" id="isPasted">An Edge-AI-Powered Automotive Sector</h3><p dir="ltr" id="isPasted">In recent years, Edge AI has proven its value as a game-changer in the automotive industry by enabling real-time performance and various optimizations across different use cases of the transport and mobility ecosystem. Such use cases are found not only within popular AI systems like autonomous vehicles but also in use cases that foster more efficient traffic management.</p><blockquote><p dir="ltr">“Innovating with Edge AI in automotive isn't just about driving smarter cars; it's about redefining the road ahead, enhancing safety, and empowering vehicles to make split-second decisions, making every journey safer and more efficient." - – Samir Jaber, Editor-in-Chief</p></blockquote><h3 dir="ltr" id="isPasted">Autonomous Vehicles: Enhancing Safety and Efficiency on the Road</h3><p dir="ltr">Autonomous vehicles are at the forefront of disruptive innovation in the transportation sector. Edge AI is vital in enabling these vehicles to navigate and make critical decisions in real time based on the fast processing of a large volume of sensor data. Edge AI technologies are deployed in all five levels of autonomous vehicles, ranging from Level 0 (no automation) to Level 5 (full automation). Specifically, Edge AI systems improve the real-time functionalities of vehicles of lower automation levels while boosting the autonomy of vehicles that fall into higher levels of automation.&nbsp;</p><p dir="ltr">For instance, Edge AI enhances the autonomy of partial-automation vehicles (i.e., Level 3) by boosting their advanced real-time perception, decision-making, and control functionalities. These functionalities help autonomous vehicles drive more safely and improve their environmental performance. Nowadays, relevant Edge AI functionalities can be implemented and deployed on different types of embedded devices of modern vehicles, such as On-Board Units (OBUs), enabling fast and real-time inference.&nbsp;</p><p dir="ltr">Furthermore, Edge AI's integration with other technological trends, such as Autonomous, Connected, Electric, and Shared (ACES) mobility, unlocks synergistic benefits for the transport ecosystem. Based on Edge AI, ACES technologies can enable collaborative intelligence functionalities close to the field. Such functionalities include, for example, route optimization, congestion reduction, and enhanced energy efficiency.</p><p dir="ltr"><span class="fr-img-caption fr-fic fr-dib" style="width: 769px;"><span class="fr-img-wrap"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1710792265113-23.jpg" style="width: 100%px;" class="fr-fic fr-dib"><span class="fr-inner">The Holon Mover, an autonomous mini-bus (Credit: Benteler)</span></span></span></p><h3 dir="ltr" id="isPasted">Real-Time Traffic Management and Smart Parking</h3><p dir="ltr">Real-time optimizations are at the heart of efficient traffic management systems, enabling dynamic adaptations to rapidly changing conditions. Here, Edge AI empowers traffic management systems to process data from various sources (e.g., sensors, cameras, transport infrastructure) in real time. Analyzing such data at the network's edge enables the development of intelligent traffic management solutions that optimize signal timings, prioritize emergency vehicles, and dynamically adjust traffic flows. This way, Edge AI solutions help reduce congestion and improve transport efficiency.</p><p dir="ltr">The real-time data processing functionalities of Edge AI are essential in use cases that involve busy intersections where split-second decision-making is required. For such cases, Edge AI enables the development of intersection control systems that identify and prioritize vehicles while being able to detect anomalies or potential hazards. Such functionalities are key to ensuring smooth and safe traffic flows.</p><p dir="ltr">Furthermore, finding parking spaces in urban environments is one of the most pressing challenges, especially in highly populated megacities. With Edge AI, smart parking systems can provide real-time feedback about parking positions based on on-device AI. These systems leverage edge devices, such as parking sensors and cameras, to monitor parking occupancy and guide drivers to available spaces efficiently and in a timely fashion. Hence, Edge AI's processing power on edge devices enables smart parking systems characterized by low latency and enhanced real-time decision-making capabilities, improving the overall parking experience for drivers.</p><h3 dir="ltr" id="isPasted">Electric Vehicles: Enhancing Battery Management and Charging Infrastructure Optimization</h3><p dir="ltr">During the last few years, the world has increasingly embraced electric vehicles (EVs). EVs require battery management optimizations and the availability of EV charging infrastructures. To this end, Edge AI can facilitate real-time monitoring and analysis of EV batteries in ways that enhance their performance, lifespan, and overall reliability.&nbsp;</p><p dir="ltr">Moreover, thanks to Edge AI algorithms, EVs can dynamically adjust charging patterns based on factors like battery condition, power availability, and user preferences. At the same time, Edge AI's integration with EV charging stations enables intelligent load management, balances energy demand, and optimizes charging schedules. Also, the proper distribution of the computational load at the edge can improve the grid's stability and efficiency by reducing strain during peak demand.</p><p><span class="fr-img-caption fr-fic fr-dib" style="width: 769px;"><span class="fr-img-wrap"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1710792305637-24.jpeg" style="width: 100%px;" class="fr-fic fr-dib"><span class="fr-inner">The Canada Infrastructure Bank (CIB) and Quebec-based charging network operator FLO have announced a plan to install over 2,000 public DC fast charger ports across the country by 2027 (Credit: FLO).</span></span></span></p><h3 dir="ltr" id="isPasted">Fleet Management and Traffic Sign Recognition</h3><p dir="ltr">Edge AI empowers fleet management systems to collect and process a wealth of vehicle data, including location, fuel consumption, driver behavior, and vehicle health. The processing of this data at the edge provides fleet managers with real-time insights into fleet performance for implementing routing optimizations, reducing fuel consumption, and enhancing the operational efficiency of their fleet. Thus, Edge-AI-driven fleet management solutions can effectively help with cost reduction, improved logistics, and enhanced customer experience.</p><p dir="ltr">Similarly, driver safety and compliance rely primarily on traffic sign recognition. Edge AI systems enable vehicles to detect and interpret traffic signs in real time. As such, they can provide drivers with real-time information about speed limits, traffic rules, and potential hazards. In this direction, advanced image processing algorithms can be deployed at the edge based on Edge AI approaches like TinyML for on-device image analysis. Therefore, Edge AI systems can enhance driver awareness, reduce the risk of accidents, and foster compliance with traffic regulations.</p><p dir="ltr">The above-listed use cases indicate how Edge AI transforms the transport sector to enable real-time functionalities across various use cases. With its ability to process vast amounts of data at the edge, Edge AI empowers transport stakeholders to enhance safety, optimize operations, and improve the overall efficiency of modern transportation systems. Moreover, it helps reduce the attack surface of transport applications and limit the amount of potentially sensitive data (e.g., driving patterns information) from being shared within a cloud infrastructure. Edge AI can also boost privacy and data protection for consumer-facing applications in the transport sector. In the future, Edge AI systems will continue to play a significant role in shaping the evolution of the automotive and transportation industry.</p>

Automotive and Transportation

<h3 dir="ltr" id="isPasted">Where Gen AI Meets Edge Computing</h3><p dir="ltr" id="isPasted">The convergence of generative AI and edge computing is revolutionizing how AI interacts with its environment. Enterprises are increasingly deploying computing resources at the edge to capitalize on local data collection, filtering, aggregation, analysis, and generation. The trend of deploying generative AI at the edge is driven primarily by two key advancements: the development of relatively small, specialized models tailored for edge deployment and the availability of hardware acceleration for inferencing processes.&nbsp;</p><p dir="ltr">These advancements enable the deployment of generative AI in edge environments, leading to applications such as voice-assisted shopper suggestions in retail, translation and emotion analysis of customer feedback, and autonomous decision-making in industrial environments and warehouses. In this chapter, we’ll take a deeper look at large language models (LLMs), what hinders their deployment at the edge, and how leading companies are finding ways to overcome these challenges.</p><blockquote><p dir="ltr">“Edge AI is changing dramatically; the new generation of hardware is capable of 500 times better performance than before, which has a massive impact on the world. It's difficult to fathom the full potential, but we do know that our ability to create accurate, high-performance Edge AI models is hugely expanded.” &nbsp;– Alexander Samuelsson, CTO of Imagimob</p></blockquote><h3 dir="ltr" id="isPasted">LLMs at the Edge: The Challenges</h3><p dir="ltr">The emergence of ChatGPT in November 2022 has led to a surge of interest in LLMs and their applications in various sectors. LLMs have revolutionized natural language processing (NLP) and natural language understanding (NLU) by enabling general-purpose conversational interactions while achieving remarkable performance on various language tasks. These models, such as GPT-3, Bard, LLaMa2, GPT-4, and Gemini, leverage neural networks with billions of parameters trained on vast amounts of data.&nbsp;</p><p dir="ltr">This enables LLMs to generate coherent and contextually relevant text, which drives the development of many innovative applications. These applications are usually deployed within cloud-based infrastructures, as the growing size of LLMs poses challenges that inhibit their deployment on edge devices. The most prominent challenge to deploying LLMs at the edge is their sheer model size. LLMs with billions of parameters require substantial computing resources, which exceed the computational capabilities of most edge devices like smartphones or IoT devices. For instance, most IoT devices have limited memory and processing power, which makes it difficult to store and execute large models efficiently. Hence, deploying massive models on edge devices requires significant optimization efforts and hardware upgrades.</p><p dir="ltr">Another major obstacle is the computational power required to utilize LLMs in real-time interactions. Edge devices often lack the computational capacity to process complex language models quickly. Therefore, applications requiring real-time interactions (e.g., chatbots, voice assistants) may not be feasible on edge devices with limited processing capabilities. The deployment of LLMs on such devices asks for sophisticated strategies to reduce latency and enhance performance.</p><p dir="ltr">Despite these challenges, the concept of edge LLMs is very promising, as it comes with significant advantages like reduced latency and enhanced user experience. For instance, voice assistants or predictive text features can run directly on the device, eliminating the need for a constant internet connection and ensuring fast response times. Moreover, Edge LLMs can greatly enhance privacy and data protection for the growing base of LLM users. When processing prompts locally on the device, there is no need to transmit sensitive user information (e.g., information about the users’ tasks) to the cloud. This reduces the potential risks associated with data breaches or unauthorized access to user data.&nbsp;</p><h3 dir="ltr">Enabling LLMs and Edge Computing Convergence</h3><p dir="ltr">Nowadays, different solutions are under research and development to address the challenges of LLM deployment at the edge. These include model compression techniques, specialized hardware accelerators, and on-device training methods. They aim to minimize model size, reduce computational requirements, and achieve efficient execution on edge devices.</p><p dir="ltr">One of the most prominent approaches to deploying LLMs on edge devices is reducing their model size. This can be achieved through techniques such as pruning, quantization, and distillation to produce smaller models while maintaining high performance. Pruning involves removing redundant parameters from the model, which results in a more compact representation. Quantization reduces the precision of numerical values used for computation, thus requiring less memory and processing power. Distillation compresses large models into smaller ones by using the output of one model as the target of another, leading to significant reductions in model size.</p><p dir="ltr">Another approach involves the design and development of domain-specific models. Domain-specific applications enable the deployment of smaller and highly optimized models customized for particular use cases yet offer better efficiency and accuracy. Furthermore, domain-specific models enable edge devices to benefit from LLMs’ capabilities without relying on cloud infrastructures, which leads to increased privacy and faster response times.</p><p dir="ltr"><span class="fr-img-caption fr-fic fr-dib" style="width: 769px;"><span class="fr-img-wrap"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1710792414754-26.jpg" style="width: 100%px;" class="fr-fic fr-dib"><span class="fr-inner">BrainChip’s AKD1000 AIoT chip brings AI and edge together, leveraging its Akida™ advanced neural networking processor (Credit: BrainChip)</span></span></span></p><h3 dir="ltr" id="isPasted">Examples of Real-Life Edge LLM Systems</h3><p dir="ltr">One example of an LLM deployed on edge devices is an MIT-developed model called&nbsp;<a href="https://hanlab.mit.edu/blog/tinychat" target="_blank">TinyChat</a>. TinyChat is a neural conversational model that uses a small, domain-specific model to achieve real-time interactions on edge devices. The model is trained on data from a specific domain, such as customer support or technical assistance, which makes it more efficient and accurate in handling domain-specific queries.</p><p dir="ltr">NVIDIA has also developed an embedded AI platform called&nbsp;<a href="https://www.nvidia.com/en-us/edge-computing/products/igx/" target="_blank">NVIDIA IGX Orin Developer Kit</a>, specifically designed to deploy LLMs on the edge. It offers high-performance computing power and memory capacity, making it ideal for running large models locally on devices like cars or robots. The kit uses NVIDIA's&nbsp;<a href="https://developer.nvidia.com/tensorrt" target="_blank">TensorRT</a> high-performance deep-learning inference framework. The latter optimizes models for deployment on edge devices towards faster inference times and lower power consumption.</p><p dir="ltr">Qualcomm is also working on optimizing <a href="https://www.qualcomm.com/news/onq/2023/11/accelerating-generative-ai-at-the-edge" target="_blank">generative AI for edge devices</a>. Their efforts enable large models to perform real-time inferences on low-powered devices, which reduces applications’ reliance on cloud infrastructures. Qualcomm uses techniques such as on-device model compression and hardware acceleration to overcome the challenges of deploying LLMs on edge devices.</p><table style="width: 100%;"><tbody><tr><td style="width: 100%;">CASE STUDY: AXELERA<h2>Generative AI Meets Axelera's In-Memory Computing at the Edge</h2>At its core, Generative AI’s ability to craft new data boosts new applications. Large Language Models (LLMs) like GPT-4 drive conversations, and tools like Dall-E convert natural language input into images. A fast-evolving extension of this development is multi-modal Generative AI, where different input sources of text, audio, and video are analyzed and acted upon to generate new data.&nbsp;<br>For businesses across the board, AI technology marks a transformative era, enabling dynamic optimization of operations for enhanced efficiency and environmental sustainability. For example, in surveillance and security, along with other sectors that rely heavily on vast volumes of text, audio, and video data, multi-modal Generative AI is set to introduce formidable capabilities for real-time anomaly detection and proactive intervention to prevent incidents from escalating. Furthermore, surveillance footage becomes queryable through simple prompts, allowing for comparing different feeds, generating situational descriptions, and providing simulated outcomes. In parallel, the manufacturing sector leverages machine vision and automation, enhanced by digital twin simulations, while the retail industry is witnessing the emergence of automated checkouts. Moreover, AI’s role extends to facilitating personalized shopping experiences and advancing smarter inventory management practices.&nbsp;<br>How does this impact AI processing at the edge? Today, most of the AI processing is done in centralized data centers. However, compared to typical business computing, AI processing is considerably more compute- and data-demanding and typically resorts to using powerful but power-hungry GPUs (Graphics Processing Units). This creates sustainability challenges, heavy demands on network bandwidth, real-time delays, and privacy concerns.&nbsp;<br>Enter Generative AI processing at the edge. Localized AI processing, closer to where the data is generated, can enhance privacy by not sending sensitive data to the cloud. Edge AI can also efficiently meet scalability challenges due to its more distributed model. However, implementing Generative AI at the edge presents new challenges, particularly in resource- and cost-constrained environments. As AI demands substantial processing power, a new breed of scalable, purpose-built AI acceleration solutions is emerging. Such novel technologies, like Axelera’s Digital In-Memory-Computing, can deliver remarkably high throughput while supporting programming flexibility, yet process AI models at a fraction of the cost and power consumption compared to traditional GPUs.<br>The evolution of Generative AI at the edge heralds a significant transformation, necessitating a careful equilibrium between the opportunities it presents and the responsibilities it entails. It’s imperative to equip the workforce for this transition, ensure AI development is guided by ethical principles, and guarantee that the fruits of this technological evolution are accessible to all.</td></tr></tbody></table><h3 dir="ltr" id="isPasted">LLM at the Edge: Transforming Multiple Industries at Once</h3><p dir="ltr">Edge LLM deployments are likely to have a significant impact on various markets and application areas, bringing advanced conversational AI capabilities to the edge in real time. Here are some specific applications where Edge LLMs are expected to make a significant impact:</p><ul><li dir="ltr"><p dir="ltr">Chatbots:&nbsp;Edge LLMs can improve the capabilities of state-of-the-art chatbots by enabling them to generate more intelligent and contextually relevant responses directly on edge devices. This reduces latency and the need for an internet connection, leading to faster and more efficient user interactions.</p></li><li dir="ltr"><p dir="ltr">Virtual Assistants for Front Office Applications:&nbsp;Edge LLMs can enhance virtual assistants and front office applications by allowing them to process user queries locally. This is particularly useful in scenarios requiring real-time conversation, such as with voice assistants.</p></li><li dir="ltr"><p dir="ltr">Code Generation and Code Completion:&nbsp;Edge LLMs can greatly benefit developers by providing code suggestions, code completion, and automatic code generation directly in their development environment. This can streamline the software development process, leading to increased productivity and efficiency in tasks such as Continuous Integration and Continuous Development (CI/CD) and other DevOps support tasks.</p></li><li dir="ltr"><p dir="ltr">Text Generation by IoT devices:&nbsp;When deployed in cloudless environments like rural areas, IoT devices can leverage Edge LLMs to generate text in real time. For example, a smart agriculture device can analyze sensor data locally and provide recommendations on crop management, irrigation, or pest control without relying on an internet connection.</p></li><li dir="ltr"><p dir="ltr">Training with Real-time Feedback: Edge LLMs can enable on-device training with real-time feedback. This is particularly beneficial in applications such as Augmented Reality (AR) and Human-Robot Collaboration (HRC), where instant feedback and adaptation are crucial for providing a pleasant and satisfactory user experience. For example, an AR application can use an Edge LLM to generate realistic virtual objects based on real-time information captured by the device's camera.</p></li></ul><table style="width: 100%;"><tbody><tr><td style="width: 100%;">CASE STUDY: BRAINCHIP<h2>Scaling Generative or Multi-modal AI</h2>The Generative AI phenomenon and the related Multi-modal AI have opened a new way of services and business along with an enormous challenge of exponentially increasing costs for training and inference. Activities like ASR (Automatic Speech Recognition) through raw audio, Text-to-Speech (and vice versa), and, with the advent of Augmented Reality, more contextual scene generation are among many possibilities driving demand for compute.&nbsp;<br><br>The challenge is that the level of sensor data, LLMs, and Large Vision Models (LVMs) is too heavy to manage on the Edge device. Therefore, it drives a much larger load at the cloud, not to mention the challenges with real-time response, security, and privacy. With the slowing down of Moore’s Law and especially Dennard’s Scaling, power and energy benefits that were taken for granted don’t provide the expected gain. Therefore, there is a great deal of architectural innovation needed to empower Edge devices.&nbsp;<br><br>BrainChip’s DNA is that of brain-inspired, neuromorphic computing, but taking it in a digital and event-based manner rather than traditional analog. In the second generation, BrainChip introduced the Temporal Event-based Neural Networks (TENNs) that are very adept at multi-dimensional streaming data. Initially, TENNs have shown their benefit for a wide variety of streaming data solutions – consuming raw audio signals or health care data without the need for filtering to infer audio or vital signs. Similarly, they show benefits in achieving video object detection in sub-watt power envelopes.&nbsp;<br><br>Combining the new algorithms with some innovative hardware choices – especially those that make 3D convolutions very efficient – brings a big step in efficiency—demonstrating more than 100-500x improvement in energy efficiency without compromising accuracy. In terms of disruptive potential, TENNs could revolutionize LLMs and LVMs at the Edge. Tested on prior generation transformer-based models, a TENNs-equivalent has shown that for equivalent perplexity scores (an indicator of correctness), while reducing model size and MACs/token by 3-4 orders of magnitude. More importantly, the training of these models is similar to that of CNN training and yet takes less than 1/10th the time compared to the transformer equivalent.&nbsp;<br><br>The result is that AkidaTM with TENNs can provide radical alternatives for Edge Devices that can now handle much more complex models in a small footprint solution for vision, surveillance, hearables, automotive, healthcare, and more. Check out our <a href="https://brainchip.com/wp-content/uploads/2023/06/TENNs_Whitepaper_Final.pdf" target="_blank" rel="noopener noreferrer" data-lf-fd-inspected-kn9eq4roawkarlvp="true">white paper</a> from BrainChip.</td></tr></tbody></table><p dir="ltr" id="isPasted">Overall, the combination of generative AI and edge computing in hybrid edge-cloud deployments offers a wide range of benefits in terms of reduced latency, improved scalability, enhanced privacy, and cost efficiency. Edge AI vendors have already developed and demonstrated Edge LLM solutions, including their benefits in real-time applications like chatbots, virtual assistance and human-robot collaboration. As more Edge LLM applications emerge across various industries, the integration and use of edge and cloud resources with LLMs will play a crucial role in unlocking the full potential of generative AI.</p>

Generative AI at the Edge

Edge AI Challenges and Real-World Mitigations

The Future of Edge AI

Exploring the Dynamic World of Edge AI Applications Across Industries


2024 State of Edge AI Report

<p dir="ltr" id="isPasted">Our new report, <strong>AI at the Edge: Technology Accelerated by Wi-Fi Services</strong>, dives deeply into the convergence of Wi-Fi services and Edge AI and explores how a technology model like WaaS can accelerate the adoption of AI at the edge.&nbsp;</p><p dir="ltr">Below is an excerpt from Chapter 1 of the report. To read the full report, including in-depth case studies, download it now.</p><p dir="ltr"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzA5MTI3Njg4OTg0LVJlbGF5IDIucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib"></p><p dir="ltr"><span contenteditable="false" draggable="true" class="fr-video fr-deletable fr-fvc fr-dvb fr-draggable"><iframe width="540" height="305" src="https://c8056756.sibforms.com/serve/MUIFAIgRr7nh2u88k57MUBWp43e1lkOKIXq3Z9dxFiC8kXdgaxMxkxsZKsum_gd4u7Qq4_1n-e3n6hH4X_Nw12RLp7SUWPiUkUUgLHhq84lH8VfbGGhwuM1Hvkr60WpcS1SyyBLR-7ErbMPEfKrwqo_-_-CHDFwvTGW-swKGBpTKYdEO-rdhIbTFZyt7qA8fH9zg-zqRf6Li7cOQ" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span></iframe></span></p><p dir="ltr" id="isPasted">Typically, artificial intelligence has been powered by cloud computing, given AI’s need for computing power and data-intensive processing. However, with the advent of edge computing, the AI landscape has been shifting, allowing for the rise of Edge AI. Edge AI is a revolutionary approach to how we deploy AI in various application areas, as it enables local computing and processing of data. That means the computing power is brought closer to the source of data, facilitating on-device data processing. With Edge AI, the system can make decisions in real time and with full contextual awareness by enabling AI features and capabilities directly on devices. Such a process would benefit from significant advantages brought about by Edge AI, including quick response, high bandwidth efficiency, and cost-effectiveness, to name a few.</p><p dir="ltr">Unlike Cloud AI, which relies on remote servers for data processing and analysis, Edge AI is applied locally among a spectrum of devices spanning from data centers to IoT endpoints. The nature and location of the data being processed determine whether Cloud AI or Edge AI is more suitable for a particular application. For processing-intensive applications where latency can be compromised for the sake of in-depth data analysis and powerful computation, Cloud AI would be the ideal option. However, many applications are time-sensitive and require real-time data processing and prompt responses. This is where Edge AI takes center stage, minimizing the need to send data back and forth to a cloud server. This can take place at any location denoted as the edge, such as retail stores, hospitals, traffic lights, autonomous vehicles, and educational facilities.</p><p dir="ltr" id="isPasted">The following table explains the benefits and applicability of Edge AI and Cloud AI and how they differ from one another. Keep in mind that Edge AI and Cloud AI are not interchangeable technologies but rather applicable in different applications and scenarios. In fact, Edge AI and Cloud AI can even be complementary for systems that include both time-dependent and processing-intensive applications, which can benefit from a hybrid approach to data processing that integrates both technologies, as we’ll discuss later in this report.</p><p dir="ltr"><em>Table 1: Applicability and Benefits of Edge AI and Cloud AI</em></p><p dir="ltr"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzA4NTg0MTM3NjM2LXRhYmxlIHJlbGF5LnBuZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 100%px;" class="fr-fic fr-dib"></p><h3 dir="ltr" id="isPasted">The Evolution and Adoption of Edge AI</h3><p dir="ltr">A 2023 report by Gartner on innovations in and around AI has highlighted the AI hype cycle for this year, showcasing the various advancements in AI and where they are today in terms of the public’s expectations . Among these innovations, we can see Cloud AI slowly navigating the so-called “Slope of Enlightenment” stage, where it is gradually transitioning toward mainstream adoption. In fact, Gartner expects Cloud AI to become mainstream in two to five years. Meanwhile, one of the fastest-growing innovations we can pinpoint on the cycle graph is Edge AI, showing a fast adoption rate to reach mainstream productivity in less than two years. This validates the potential that Edge AI has demonstrated in the past 2-3 years and shows that both edge and cloud computing can support AI applications in a symbiotic manner in the very near future.</p><p dir="ltr"><span contenteditable="false" class="fr-img-caption    fr-fic fr-dib fr-draggable" style="width: 768px;" draggable="false"><span class="fr-img-wrap"><a href="https://www.gartner.com/en/articles/what-s-new-in-artificial-intelligence-from-the-2023-gartner-hype-cycle" target="_blank" rel="noopener noreferrer"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzA4NTg0MjE0MDU5LTAzXzExLmpwZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 768px;" class="fr-fic fr-dib"><span class="fr-inner" contenteditable="true">The hype cycle for AI in 2023 showed the rapid adoption rate of Edge AI. Credit: Gartner.</span></a></span></span></p><p dir="ltr" id="isPasted">The recent developments in Edge AI have opened up new opportunities for devices to operate with human-like intelligence, regardless of their location. The successful deployment of AI models at the edge can be attributed to innovations that have significantly contributed to today’s digital transformation. For instance, neural networks and AI infrastructure have reached a level of maturity enough to pave the way for generalized machine learning. Organizations are now able to train AI models and deploy them in production at the edge. Besides, the recent advanced Edge AI accelerator chipset development, such as Axelera Metis, with high performance and low power budget, has made it possible to integrate these AI capabilities at a low power budget and with very high inference speed. Relay2 is pioneering this embedded integration with Wi-Fi Access Point, the edge of the Wi-Fi network. &nbsp;This is further strengthened by the widespread adoption of IoT devices, which is arguably the key reason behind the blowup of big data. Businesses can now collect every type of data from various sources. This availability of data and corresponding devices has made it possible to deploy AI models successfully at the edge.</p><p dir="ltr" id="isPasted">Given their remarkable ability to process and comprehend a range of unstructured data, including visuals, audio, temperature, language, faces, and more, AI algorithms have emerged as effective solutions for problems that end users face in real-time. Yet, deploying these AI applications primarily in centralized data centers like the cloud may prove impractical, especially with concerns about latency, bandwidth, and privacy. This is where the benefits of edge applications can facilitate AI deployment with their localized nature and decentralization, enabling real-time insights, high availability, and enhanced privacy.&nbsp;</p><p dir="ltr">Nonetheless, Edge AI is not free of limitations, either. It still faces challenges, especially in data management, integration, security, miniaturization, and scalability. To overcome these challenges, a harmonious and purposeful co-design of hardware and software is necessary, and coordination with other technologies can prove quite fruitful. In fact, one of the most promising solutions for Edge Intelligence comes in the form of the convergence of Edge AI with cloud-based Wi-Fi Services, as we’ll see in the following section. Achieving miniaturization, model accuracy, energy efficiency, and cost-effectiveness simultaneously is a complex challenge. However, with proper coordination, optimization, and communication, we can facilitate the emergence of the next set of AI advancements at the edge.</p><h3 dir="ltr" id="isPasted">Next-Level Intelligence: Reinforcing Edge AI with Wi-Fi Services</h3><p dir="ltr">Since its inception about two and a half decades ago, Wi-Fi has shown significant development and adoption, with connection speeds skyrocketing from a few megabits to multi-gigabits per second. With this increase in speed, we have also seen an exponential rise in Wi-Fi-enabled devices, with almost every mobile device and computer now including Wi-Fi by default. In order to support the growing usage, demands, and throughputs of Wi-Fi, the network architecture has played a crucial role. Initially, Wi-Fi deployments were based on standalone access point architectures but later shifted to centralized hardware-based controller architectures. However, along with Wi-Fi functionalities like network optimization and mobility came some challenges, including scalability, reliability, manageability, and bottlenecks in performance, especially with increasing Wi-Fi traffic and link speeds. To tackle these challenges, service providers like Relay2 have been harnessing the power of the cloud and enabling vendors to shift from on-premise hardware controllers to cloud-native wireless controllers. Such a cloud-native system can provide the same benefits of centralized management without the constraints of physical hardware or associated adaptations. This not only enables greater flexibility but also offers more efficient and cost-effective management and coordinated control. As a result, this space has allowed for the rise of Wi-Fi-as-a-Service (WaaS), a subscription-based service that integrates software, infrastructure, and managed services to enable centralized management and coordinated control across different access points and automation of network monitoring and troubleshooting.</p><p dir="ltr">Bringing Wi-Fi services and Edge AI into the same picture, we can see how their benefits align, where Wi-Fi enhances Edge AI’s scalability and integration capabilities, and Edge AI furthers the user experience of Wi-Fi solutions with data-driven insights and real-time analysis. For instance, looking at Edge AI’s integration challenges, we can still see cases of incompatibility stemming from differences in hardware, software, and communication protocols, which result from the unique architecture, specifications, and interface of every edge device. Employing Wi-Fi at this level enables secure, wide-coverage wireless communications, which further promotes interoperability and minimizes compatibility issues. Similarly, scalability challenges in terms of how Edge AI systems are handling increasing amounts of data and devices may compromise efficiency. The adaptability and scalability brought about by Wi-Fi services can help overcome these hurdles by enlarging a business’s network infrastructure and enabling effortless infrastructure management to meet the growing demand and shifting requirements.</p><p dir="ltr">In other words, tying wireless and edge computing together can empower AI applications and reshape the business landscape to drive the digital transformation we are experiencing today. This convergence of Wi-Fi and edge computing presents a promising frontier for innovation, efficiency, and growth. As companies continue to recognize the value of this convergence, we can anticipate a marked increase in its adoption. As a result, edge computing would bring about a new wave of intelligent AI applications by integrating more computing power, which would allow adaptive learning and real-time responsiveness. This would be a significant step towards a more resilient system as the demand for always-on services continues to grow across various industries and application areas, including education, smart buildings, intelligent traffic management, smart agriculture, and smart retail. As more companies embrace the convergence of Wi-Fi and edge computing, we can expect to see a new era of technological innovation and efficiency.&nbsp;</p><p dir="ltr">Right at the center of this technological fusion is Relay2, a leading Wi-Fi service provider that is on a mission to challenge the status quo of today’s Wi-Fi access point market. Relay2 is at the vanguard of the Wi-Fi edge-computing revolution, powered by their innovative ServiceEdge Platform, a Service Points technology that provides unparalleled performance, scalability, security, and privacy across a wide range of edge applications. In the next chapter, we will dive deeper into Relay2’s Wi-Fi services and explore how Relay2 is poised to reshape the Wi-Fi industry and how its patented platform is enabling more AI applications and capabilities within the edge-computing framework.</p><p dir="ltr">This was an exceprt of Chapter 1 of the AI at the Edge: Technology Accelerated by Wi-Fi Services report. To read the full report, including in-depth case studies, download it below. &nbsp;</p><p dir="ltr"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzA5MTI3ODU2MTAyLVJlbGF5IDIucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib"></p><p dir="ltr"><span contenteditable="false" draggable="true" class="fr-video fr-deletable fr-fvc fr-dvb fr-draggable"><iframe width="540" height="305" src="https://c8056756.sibforms.com/serve/MUIFAIgRr7nh2u88k57MUBWp43e1lkOKIXq3Z9dxFiC8kXdgaxMxkxsZKsum_gd4u7Qq4_1n-e3n6hH4X_Nw12RLp7SUWPiUkUUgLHhq84lH8VfbGGhwuM1Hvkr60WpcS1SyyBLR-7ErbMPEfKrwqo_-_-CHDFwvTGW-swKGBpTKYdEO-rdhIbTFZyt7qA8fH9zg-zqRf6Li7cOQ" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe></span><br></p>

Intelligence at the Edge: Bringing AI Closer to You

AI at the Edge Report. Chapter 1: Wi-Fi Analytics Services Enabling Edge AI

<h1 dir="ltr" id="isPasted">Introduction</h1><p dir="ltr">Artificial intelligence (AI) is experiencing a significant transition with the increasing adoption of edge computing, where data processing occurs closer to the data source. The aim is to enable real-time decision-making and reduce dependency on cloud services.&nbsp;</p><p dir="ltr">This transition is driven by advanced microprocessors, with the Texas Instruments AM68A being a notable example. It offers a combination of efficiency, power, and adaptability, making it well-suited for the requirements of Edge AI applications.&nbsp;</p><p dir="ltr">In this article, we uncover the capabilities and implications of the Texas Instruments AM68A within the Edge AI landscape. Our exploration will dive into the intricacies of its technological features and spotlight its integration with the&nbsp;<a href="https://edgeimpulse.com/" target="_blank">Edge Impulse</a> platform. We'll also feature some case studies that demonstrate the practical application of the AM68A in Edge AI projects focused on computer vision.<br><br><span contenteditable="false" draggable="true" class="fr-video fr-deletable fr-fvc fr-dvb fr-draggable"><iframe src="https://c8056756.sibforms.com/serve/MUIFAMyA6BGksOUPEokHuBnEmZRHhYgJ4RXb4BV0gvzN98ymNIvQdTcBFQnRVfviT6yZSFW_R2g1hENBu7p7Afnyv9Kt0unAAbfFU5mMQhBwI-8hQnKf2r1mmRKhUgDLmtk8bpVSZ7kZGpleCPqtYRv8_kkvODL7F4neGLNGgdpFDm9pPzoYpwbQkc0BU5GVOFoq8CSmD0DDvDjG" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block; margin-left: auto; margin-right: auto; max-width: 100%; width: 766px; height: 603px;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe></span></p><h1 dir="ltr"><br></h1><h1 dir="ltr">Overview of the Texas Instruments AM68A</h1><p dir="ltr">The Texas Instruments AM68A is a microprocessor that delivers great performance and features dedicated AI hardware acceleration. This enhancement is crucial for optimizing AI tasks directly on the device, enhancing efficiency and processing speed.&nbsp;</p><p dir="ltr">Before exploring its diverse uses and influence in Edge AI, it's essential to understand its core specifications and features. This groundwork will provide a clear perspective on the microprocessor's capabilities and how it can be utilized in various Edge AI contexts.</p><p dir="ltr"><span class="fr-img-caption fr-fic fr-dib" style="width: 304px;"><span class="fr-img-wrap"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzA0ODg1MDY3ODI1LWltYWdlMi5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 302px;" class="fr-fic fr-dib" alt="Texas-Instruments-TI-AM68A"><span class="fr-inner" spellcheck="false">Fig. 1: Texas Instruments AM68A development platform</span></span></span></p><p dir="ltr" id="isPasted">Here are some core specifications and features of the AM68A:</p><div dir="ltr" align="left"><table><tbody><tr><td><p dir="ltr"><strong>Feature</strong></p></td><td><p dir="ltr"><strong>Description</strong></p></td></tr><tr><td><p dir="ltr"><strong>Processor Core</strong></p></td><td><p dir="ltr">Dual 64-bit ARM Cortex-A72 microprocessor subsystems, up to 2 GHz, with a shared 1MB L2 cache.</p></td></tr><tr><td><p dir="ltr"><strong>Deep Learning Accelerators</strong></p></td><td><p dir="ltr">Deep Learning Accelerator capable of up to 8 TOPS, for efficient AI computations and real-time data processing.</p></td></tr><tr><td><p dir="ltr"><strong>Vision Processing</strong></p></td><td><p dir="ltr">Vision Processing Accelerators, including an Image Signal Processor and multiple vision assist accelerators for handling image and video data.</p></td></tr><tr><td><p dir="ltr"><strong>Additional Processing Units</strong></p></td><td><p dir="ltr">Dual-core ARM Cortex-R5F MCUs for general compute operations and device management.</p></td></tr><tr><td><p dir="ltr"><strong>Graphics Processing</strong></p></td><td><p dir="ltr">IMG BXS-4-64, a high-performance 3D GPU supporting OpenGL ES 3.1 and Vulkan 1.2 for graphic-intensive applications.</p></td></tr><tr><td><p dir="ltr"><strong>Connectivity Options</strong></p></td><td><p dir="ltr">Gigabit ethernet, USB 3.0, CSI 2.0, PCIe gen 3, and various memory interfaces.</p></td></tr><tr><td><p dir="ltr"><strong>Security Features</strong></p></td><td><p dir="ltr">Secure boot and crypto hardware accelerators for robust security.</p></td></tr></tbody></table></div><p dir="ltr"><em>Table 1: Specifications and features of Texas Instruments AM68A</em></p><h1 dir="ltr">The Role of Deep Learning Accelerators in Edge AI</h1><p dir="ltr">The Texas Instruments AM68A has deep learning accelerators, which play a crucial role in Edge AI and computer vision applications. These are specialized hardware components designed to efficiently execute the computations required for deep learning algorithms. Unlike general-purpose processors, these accelerators are optimized for specific tasks like matrix multiplications and convolutions, which are fundamental in neural network operations.</p><p dir="ltr">The significance of deep learning accelerators in edge computing lies in their ability to bring advanced AI functionalities to devices operating at the edge of networks. By handling complex computations locally, these accelerators reduce the need to transmit large amounts of data to the cloud for processing. This cuts down on latency, making real-time processing feasible, and addresses concerns like bandwidth limitations and data privacy.</p><h1 dir="ltr">Real-World Applications and Case Studies</h1><p dir="ltr">The Texas Instruments AM68A has been central to a variety of computer vision projects, demonstrating its versatility and capability. These case studies showcase the practical use of the AM68A in diverse Edge AI applications.</p><h2 dir="ltr">American Sign Language (ASL) Translation</h2><p dir="ltr">One of the remarkable projects developed using a Texas Instruments board is an American Sign Language (ASL) Translation system. This application utilizes the AM62A, the entry-level alternative to the AM68A, and is designed to translate ASL into text in real-time. By leveraging machine learning algorithms and image processing capabilities, this system enhances communication accessibility for the deaf and hard-of-hearing community.&nbsp;</p><p dir="ltr"><span class="fr-img-caption fr-fic fr-dib" style="width: 302px;"><span class="fr-img-wrap"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzA0ODg1MDk3MjkxLWltYWdlNC5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib" alt="sign-language"><span class="fr-inner" spellcheck="false">Fig. 2: Training the model to recognize different signs</span></span></span></p><p dir="ltr" id="isPasted">The project's success on the AM62A highlights the potential advancements that could be achieved with the more powerful AM68A.&nbsp;</p><p dir="ltr"><em><strong>Read more about this project:&nbsp;</strong></em><a href="https://docs.edgeimpulse.com/experts/image-projects/asl-byom-ti-am62a" target="_blank"><strong><em>Import a Pretrained Model with "Bring Your Own Model" - Texas Instruments AM62A</em></strong></a></p><h2 dir="ltr">Motorcycle Helmet Detection</h2><p dir="ltr">Another innovative application is the Motorcycle Helmet Detection project. Also developed on the Texas Instruments AM62A, this system utilizes machine learning and computer vision to detect whether motorcyclists are wearing helmets. This application is crucial for enhancing road safety and can be deployed at traffic signals or highway checkpoints.&nbsp;</p><p dir="ltr"><span class="fr-img-caption fr-fic fr-dib" style="width: 302px;"><span class="fr-img-wrap"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzA0ODg1MTIwMzY2LWltYWdlNi5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib" alt="helmet-detection-edge-ai"><span class="fr-inner" spellcheck="false">Fig. 3: Testing the motorcycle helmet detection system prototype</span></span></span></p><p dir="ltr" id="isPasted">The implementation with the AM62A demonstrates the feasibility of such safety applications, which can be further optimized with the AM68A's enhanced capabilities.&nbsp;</p><p dir="ltr"><em><strong>Explore this project in detail:&nbsp;</strong></em><a href="https://docs.edgeimpulse.com/experts/image-projects/motorcycle-helmet-detection-smart-light-ti-am62a" target="_blank"><strong><em>Motorcycle Helmet Identification and Traffic Light Control - Texas Instruments AM62A</em></strong></a></p><h2 dir="ltr">Posture Detection</h2><p dir="ltr">The Texas Instruments TDA4VM board, another counterpart of the AM68A with fewer features, has been used in a posture detection project. This application focuses on identifying incorrect postures in real-time to prevent workplace injuries. Using advanced image processing and AI, the system provides immediate feedback for posture correction.</p><p dir="ltr"><span class="fr-img-caption fr-fic fr-dib" style="width: 302px;"><span class="fr-img-wrap"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzA0ODg1MTQxOTU2LWltYWdlNS5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib" alt="posture-detection-edge-ai"><span class="fr-inner" spellcheck="false">Fig. 4: Texas Instruments AM62A is used to efficiently distinguish between correct and incorrect postures, promoting ergonomic practices and preventing injuries.</span></span></span></p><p dir="ltr" id="isPasted">The AM68A, with its superior processing power, could elevate such health and safety applications to new levels.&nbsp;</p><p dir="ltr"><em><strong>Learn more about this project:&nbsp;</strong></em><a href="https://docs.edgeimpulse.com/experts/image-projects/ti-tda4vm-posture-detection" target="_blank"><strong><em>Correct Posture Detection and Enforcement - Texas Instruments TDA4VM</em></strong></a></p><h2 dir="ltr">Smart Factory Prototype</h2><p dir="ltr">Finally, the Smart Factory Prototype project, developed on the Texas Instruments TDA4VM, illustrates the potential of these boards in industrial applications. This system employs AI to monitor manufacturing processes, enhancing efficiency and predicting maintenance needs. The project demonstrates how edge computing can revolutionize industrial operations, and with the advanced features of the AM68A, these applications could achieve even greater efficiency and accuracy.&nbsp;</p><p dir="ltr"><em><strong>Read more about the Smart Factory Prototype:&nbsp;</strong></em><a href="https://docs.edgeimpulse.com/experts/image-projects/smart-factory-prototype-ti-tda4vm" target="_blank"><strong><em>Smart Factory Prototype - Texas Instruments TDA4VM</em></strong></a></p><h2 dir="ltr">Transitioning to the AM68A</h2><p dir="ltr">Each of these projects, developed on the AM62A and TDA4VM boards, lays the groundwork for what could be achieved with the AM68A. With its enhanced processing power and deep learning capabilities, the AM68A is poised to elevate these applications, offering more efficient processing, quicker response times, and the ability to handle more complex AI models. This transition represents a significant step forward in the capabilities of edge computing devices and their applications across various industries.</p><h1 dir="ltr">Conclusion</h1><p dir="ltr">The AM68A, with its advanced deep learning accelerators and enhanced processing capabilities, stands as a significant player in the rapidly evolving landscape of edge computing. Its integration with the Edge Impulse platform has opened new avenues for AI model development, making advanced AI functionalities more accessible and effective.</p><p dir="ltr">Throughout the article, we've seen how the AM68A is being utilized in various sectors, from enhancing road safety with motorcycle helmet detection to improving workplace ergonomics through posture detection. These applications, initially developed on the AM62A and TDA4VM, offer a glimpse into the possibilities with the more advanced AM68A. The chip's potential in driving innovations in smart factories, healthcare monitoring, environmental analysis, and beyond, underscores its versatility and efficiency in handling diverse Edge AI applications.</p><h1 dir="ltr"><em>About the sponsor: Edge Impulse</em></h1><p dir="ltr"><em>Edge Impulse is the leading development platform for embedded machine learning, used by over 1,000 enterprises across 200,000 ML projects worldwide. We are on a mission to enable the ultimate development experience for machine learning on embedded devices for sensors, audio, and computer vision, at scale.&nbsp;</em></p><p dir="ltr"><em>From getting started in under five minutes to MLOps in production, we enable highly optimized ML deployable to a wide range of hardware from MCUs to CPUs, to custom AI accelerators. With Edge Impulse, developers, engineers, and domain experts solve real problems using machine learning in embedded solutions, speeding up development time from years to weeks. We specialize in industrial and professional applications including predictive maintenance, anomaly detection, human health, wearables, and more.</em></p><p dir="ltr"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzA0ODg1MTczMTQ0LWltYWdlMS5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib" alt="edge-impulse"></p><p><span contenteditable="false" draggable="true" class="fr-video fr-deletable fr-fvc fr-dvb fr-draggable"><iframe width="540" height="600" src="https://c8056756.sibforms.com/serve/MUIFAMyA6BGksOUPEokHuBnEmZRHhYgJ4RXb4BV0gvzN98ymNIvQdTcBFQnRVfviT6yZSFW_R2g1hENBu7p7Afnyv9Kt0unAAbfFU5mMQhBwI-8hQnKf2r1mmRKhUgDLmtk8bpVSZ7kZGpleCPqtYRv8_kkvODL7F4neGLNGgdpFDm9pPzoYpwbQkc0BU5GVOFoq8CSmD0DDvDjG" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe></span><br></p>

High-performance microprocessors with AI hardware acceleration are revolutionizing Edge AI, enabling advanced computer vision capabilities directly on devices for real-time data processing and analysis.

Building Robust Edge AI Computer Vision Applications with High-Performance Microprocessors

<p dir="ltr" id="isPasted">In the latest BrainChip podcast hear from Zach about Edge Impulses astounding metrics for developer recruitment, training volume, and adoption; using AI to fill the gap when data sets are small or non-existent; the challenges of bringing AI to manufacturing and real-world use cases.</p><p dir="ltr"><a href="https://www.youtube.com/watch?v=f2hD-kHssg0" target="_blank">Listen to the full episode here.</a> Some of the highlights from the conversation, worth tuning in for include:</p><h2 dir="ltr">Reducing time and effort with automated data labeling and processing</h2><p dir="ltr">One of the pivotal advancements in AI and Edge Computing is the automation of data labeling and processing. The use of big segmenter models for auto-labeling imagery and other data types has been a game-changer, significantly reducing the time and effort required in data preparation. This automation is not just a matter of convenience but a critical step in enhancing the efficiency and accuracy of AI models. For engineers, this means an accelerated process from concept to deployment, enabling a more agile response to industry needs.</p><p dir="ltr">The implications of automated data labeling extend beyond mere efficiency. It represents a shift in how engineers approach data - from a task-intensive process to a more streamlined, model-driven approach. This shift is crucial in industries where the volume of data is vast and the need for precision is paramount. The ability to quickly identify and utilize relevant data points without extensive manual intervention is a substantial leap forward in AI and Edge Computing.</p><h3 dir="ltr">The challenge of LLMs and the power of the developer ecosystem<br><br></h3><p dir="ltr">Despite the advancements, the deployment of AI models, especially Large Language Models (LLMs), in edge computing environments poses significant challenges. The reliability of these models in critical scenarios, such as industrial maintenance or medical diagnostics, is a primary concern. Engineers are tasked with ensuring that these models provide accurate and reliable outputs every time, a non-negotiable requirement in high-stakes environments.</p><p dir="ltr">The podcast highlights the growth in the developer ecosystem, particularly in platforms like Edge Impulse, which has seen its community double in size within a year. This growth is indicative of the increasing interest and adoption of AI and edge computing technologies across various sectors. Real-world deployments in healthcare, logistics, and manufacturing are testament to the practical applications of these technologies. From medical transcription to predictive maintenance in manufacturing, the scope of AI and Edge Computing is vast and continually expanding.<br><br></p><p dir="ltr">However, the journey from training to deployment is fraught with complexities. Engineers must navigate the challenges of diverse hardware environments and the unique demands of edge computing scenarios. This requires a deep understanding of both the technological and practical aspects of AI model deployment, ensuring that the solutions are not only technologically sound but also viable in real-world settings.<br><br></p><h3 dir="ltr">Beyond the hype: Making AI practical and reliable</h3><p>The industry anticipates a shift towards more practical and reliable AI solutions. The focus is expected to move away from pursuing exotic innovations to establishing clear guidelines and stable hardware for successful AI and edge computing applications. For engineers, this means a greater emphasis on developing solutions that are not only advanced but also robust and easily deployable in various environments.<br><br></p><p dir="ltr">Collaborations, like the one between Edge Impulse and Nvidia, are pivotal in this context. They bring foundational AI models to the edge, optimizing them for diverse hardware requirements. Such partnerships are crucial in bridging the gap between cutting-edge AI research and practical, deployable solutions.<br><br></p><p dir="ltr">As the technology evolves, the focus for engineers will increasingly be on creating solutions that are not just innovative but also reliable, efficient, and tailored to specific industry needs. The future of AI and Edge Computing is poised to be driven by practicality, reliability, and a deep understanding of the real-world contexts in which these technologies will operate.</p><p><a href="https://www.wevolver.com/profile/brainchip" target="_blank">Read more about BrainChip here.</a></p>

In the latest BrainChip podcast episode, host Nandan Nayampally sat down with Zach Shelby, CEO of Edge Impulse, to talk about AI at the Edge. 


BrainChip Podcast:  AI after the Hype Cycle

<p><em>This article is a summary of the full white paper "Finding Bacteria in Blood: Scaling A Hardware-Driven Neuromorphic Solution for Real-World E-Nose Applications".&nbsp;</em><a href="https://brainchip.com/finding-bacteria-in-blood-a-hardware-based-neuromorphic-solution-for-real-world-e-nose-applications/" target="_blank" rel="noopener noreferrer"><em>Read it here.</em></a></p><h2>Neuromorphic approach to replicating human senses</h2><p dir="ltr" id="isPasted">Technology has made significant strides in replicating human senses and mimicking the sense of smell. Electronic noses (e-noses), devices designed to detect and analyze odors, have become integral in medical diagnostics and food quality control. However, current e-nose technologies face portability, energy efficiency, and accuracy limitations. Recent research presents an innovative neuromorphic approach, using spiking neural networks and data-to-spike encoding, to overcome these challenges, offering a more effective and practical e-nose solution.</p><h2 dir="ltr">The Experiment: Detecting Bacteria in Blood</h2><h3 dir="ltr">Dataset Overview</h3><p dir="ltr">The 'bacteria in blood' dataset, integral to this research, was collected as part of the Mednose project at Örebro University. This dataset is pivotal for the early detection of various bacterial species in blood samples, serving as a critical tool for rapid and accurate diagnostics. It encompasses a comprehensive collection of 1200 samples, with each of the ten bacterial species represented by 120 samples. This extensive dataset is instrumental in understanding the unique chemical signatures emitted by different bacteria, which is crucial for effective diagnosis and treatment strategies.</p><h3 dir="ltr">Data Preprocessing and Encoding</h3><p dir="ltr">The preprocessing of this dataset involves baseline cancellation to eliminate background noise and scaling of sensor responses for consistency. Two innovative encoding algorithms, Address Event Representation for Olfaction (AERO) and Step Forward (SF), were employed to convert sensor data into a digital format. AERO structures sensor information into a grid, creating digital snapshots of scents. SF focuses on changes in odor data exceeding a certain threshold, inspired by the human sensory system's attention to dynamic information. These algorithms effectively capture and interpret the complex odor signatures associated with different bacteria types.</p><p dir="ltr"><a href="https://brainchip.com/akida-generations/" target="_blank" rel="noopener noreferrer">The Akida™ classifier model</a> developed in this study demonstrated a high classification accuracy of 97.42% in identifying ten different bacteria species, marking a significant advancement in electronic noses and neuromorphic computing.</p><h2 dir="ltr">Achieving High Accuracy and Efficiency</h2><p dir="ltr">In neuromorphic computing, enhancing electronic noses (e-noses) for practical applications such as detecting bacteria in the blood and optimizing the Spiking Neural Network (SNN) is crucial. This optimization is essential for effective odor detection, impacting the system's accuracy, efficiency, and speed.</p><h3 dir="ltr">Optimization Strategies</h3><p dir="ltr">The optimization of the SNN in this context involves fine-tuning various parameters and structures to maximize the efficiency of odor detection. Key aspects include adjusting the number of neurons associated with each odor class, the active connections each neuron should have, and the intensity of learning competition among neurons. This process ensures that the SNN can accurately identify patterns in the incoming data and respond rapidly, effectively mimicking the human brain's neuronal activity.&nbsp;</p><p dir="ltr">These optimizations are rooted in biological processes like Spike Time Dependent Plasticity (STDP), which allows the network to strengthen synaptic connections based on frequently encountered activation patterns. This learning mode is fundamental to how the brain adapts and responds to sensory input, making it an ideal model for neuromorphic systems like the Akida NSoC.</p><h3 dir="ltr">Performance Evaluation</h3><p dir="ltr">The performance of the optimized SNN is evaluated based on several critical factors: classification accuracy, power efficiency, and speed of odor detection. In the case of detecting bacteria in blood, the system demonstrated high mean classification accuracies, especially with the early data points in the sampling process. This indicates that the essential features for odor differentiation are present early on, allowing for rapid and accurate classification.</p><p dir="ltr">Power efficiency is another crucial metric directly impacting the system's viability in practical applications. The optimized SNN, as implemented in the Akida NSoC, shows remarkable energy efficiency, consuming minimal power while maintaining high throughput. This aspect is essential for portable or remote applications where power resources are limited.</p><p dir="ltr">The speed of odor detection is the final critical measure of the system's performance. The optimized SNN demonstrates the ability to process and classify odors swiftly, a vital feature for applications requiring real-time analysis, such as medical diagnostics or food quality control.&nbsp;</p><p dir="ltr">Optimizing the SNN for e-nose applications, as demonstrated in the Akida NSoC, represents a significant advancement in neuromorphic technology. It combines high accuracy, energy efficiency, and speed. It is a promising solution for real-world applications in healthcare diagnostics and other sectors where rapid and reliable odor detection is crucial.</p><h2 dir="ltr">Real-World Applications and Implications</h2><p dir="ltr">As demonstrated in the study "<a href="https://brainchip.com/finding-bacteria-in-blood-a-hardware-based-neuromorphic-solution-for-real-world-e-nose-applications/" target="_blank">Finding Bacteria in Blood</a>," neuromorphic technology shows immense potential in transforming healthcare and rapid disease diagnosis. This technology can significantly speed up the identification of various strains of bacteria in the blood, which is crucial for diagnosing diseases accurately and promptly. Its portability and accuracy make it an invaluable tool in remote locations or outside traditional clinical facilities, providing critical healthcare services in underserved areas.</p><p dir="ltr">Beyond healthcare, neuromorphic technology has broader applications in agriculture, environmental monitoring, and industrial processes. Its ability to mimic human sensory systems allows for advanced detection and analysis capabilities, making it a valuable tool for quality control in food production, pollutant detection, and much more.</p><h2 dir="ltr">Conclusion</h2><p dir="ltr">The advancements in neuromorphic technology, in sensory applications like electronic noses, herald a new era in AI and machine learning. Its transformative potential lies in bringing more efficient, accurate, and low-power solutions to real-world challenges. As this technology continues to evolve, it promises to revolutionize numerous industries, improving processes and outcomes across multiple sectors.</p><h2 dir="ltr">Additional Information</h2><p dir="ltr">Please refer to the white paper "<a href="https://brainchip.com/wp-content/uploads/2023/11/Bacteria-in-Blood-research-paper_Final.pdf" target="_blank" data-lf-fd-inspected-kn9eq4roawkarlvp="true">Finding Bacteria in Blood: Scaling A Hardware-Driven Neuromorphic Solution for Real-World E-Nose Applications</a>" for further reading and detailed insights. It provides an in-depth exploration of the technology and its applications. Additionally, for a broader understanding of neuromorphic applications in medicine, the paper "<a href="https://pubmed.ncbi.nlm.nih.gov/37531951/#:~:text=Neuromorphic%20engineering%2C%20which%20uses%20neural,footprint%2C%20and%20high%20bandwidth%20solutions." target="_blank">Neuromorphic applications in Medicine</a>," published on PubMed, offers a comprehensive overview.</p><h2 dir="ltr">References</h2><ol><li dir="ltr"><p dir="ltr"><a href="https://brainchip.com/wp-content/uploads/2023/11/Bacteria-in-Blood-research-paper_Final.pdf" target="_blank" data-lf-fd-inspected-kn9eq4roawkarlvp="true">https://brainchip.com/wp-content/uploads/2023/11/Bacteria-in-Blood-research-paper_Final.pdf</a></p></li><li dir="ltr"><p dir="ltr"><a href="https://brainchip.com/finding-bacteria-in-blood-a-hardware-based-neuromorphic-solution-for-real-world-e-nose-applications/" target="_blank">https://brainchip.com/finding-bacteria-in-blood-a-hardware-based-neuromorphic-solution-for-real-world-e-nose-applications/</a></p></li><li dir="ltr"><p dir="ltr"><a href="https://pubmed.ncbi.nlm.nih.gov/37531951/#:~:text=Neuromorphic%20engineering%2C%20which%20uses%20neural,footprint%2C%20and%20high%20bandwidth%20solutions" target="_blank">https://pubmed.ncbi.nlm.nih.gov/37531951/#:~:text=Neuromorphic%20engineering%2C%20which%20uses%20neural,footprint%2C%20and%20high%20bandwidth%20solutions</a>.</p></li><li dir="ltr"><p dir="ltr"><a href="https://brainchip.com/akida-neural-processor-soc/" target="_blank">https://brainchip.com/akida-neural-processor-soc/</a></p></li><li dir="ltr"><p dir="ltr"><a href="https://brainchip.com/akida-neural-processor-ip/" target="_blank">https://brainchip.com/akida-neural-processor-ip/</a></p></li></ol>

Exploring the Fusion of Neuromorphic Computing and Electronic Noses for Advanced Sensory Solutions


Neuromorphic E-Noses in Medical and Industrial Applications

<h1 dir="ltr" id="isPasted">Introduction</h1><p dir="ltr">In an age where data is the new gold, and digital infrastructure forms the backbone of our society, the importance of data security cannot be overstated. Silicon-level security refers to the protective measures embedded at the hardware level of computing devices. It is a fundamental layer that underpins the reliability and trustworthiness of everything from consumer electronics to critical national infrastructure.</p><p dir="ltr">As we usher in the era of ubiquitous computing, with billions of connected devices exchanging data every second, the stakes for robust security have never been higher. The concept of silicon-level security isn't new; however, the complexity and sophistication of threats it must mitigate have evolved dramatically. Gone are the days when simple perimeter defenses were sufficient. Today's security architects must assume a posture of 'defense in depth', where multiple layers of security measures are integrated into the silicon itself.</p><p dir="ltr">This proactive approach to security is vital as the functionality and value of devices increasingly hinge on their ability to protect and process sensitive data securely. Whether it's financial transactions, personal information, or state secrets, the common denominator is the silicon that processes and stores this data. Thus, the integrity of silicon-level security mechanisms is a paramount concern, laying the foundation for trust in the digital age.</p><p dir="ltr">In the following sections, we'll delve into the intricate world of secure interface technologies, unpack the challenges faced by modern System on Chip (SoC) designs, and explore how advancements in technology are paving the way for stronger, more resilient security measures. &nbsp;&nbsp;</p><h1 dir="ltr">The Backbone of Security: Root of Trust and Cryptography IP</h1><p dir="ltr">At the heart of silicon-level security is the concept of a 'Root of Trust' (RoT), a set of functions trusted implicitly by the system due to their foundational role in ensuring overall security. The RoT provides a secure starting point for system boot and cryptographic operations and is immune to software-based attacks because it is often implemented in hardware. This secure cornerstone handles critical tasks such as secure boot, secure firmware updates, and cryptographic key management, ensuring that only authenticated code and communications are running on the device.</p><p dir="ltr">Adjacent to the Root of Trust is the role of Cryptography IP, which includes hardware-based cryptographic engines designed to perform encryption and decryption tasks. These cryptographic modules serve as the workhorses for data protection, enabling secure data storage and transmission, and are essential for functions like secure communications, digital signatures, and user authentication.</p><p dir="ltr">Both RoT and Cryptography IP are imperative for a secure boot process, which is the first step in a secure chain of events that occur when a device is powered on. During this process, the integrity and authenticity of the software stack are verified using cryptographic checks, establishing a secure execution environment from the outset.</p><p dir="ltr">To understand their importance, consider the analogy of a bank vault. In this analogy, RoT is the combination to the vault—highly confidential and used to validate the identity of individuals attempting to access the vault. Cryptography IP, on the other hand, is like the reinforced walls and time-locked doors—structural features that enforce security protocols and prevent unauthorized access.</p><p dir="ltr">These technologies also enable secure end-to-end communication, ensuring that data remains confidential and unaltered during transmission. With the proliferation of the Internet of Things (IoT) and connected devices, securing data in transit has become as crucial as securing it at rest.</p><p dir="ltr">Despite their robustness, the implementation of Root of Trust and Cryptography IP is not without challenges. Ensuring that these security measures themselves are impervious to exploitation requires meticulous design and regular updates in response to the evolving threat landscape. In the next section, we will examine the security challenges inherent in modern SoC designs and discuss how these foundational technologies can help mitigate those risks.</p><h1 dir="ltr">Securing the Core: Challenges in SoC Designs</h1><p dir="ltr">Modern Systems on Chip (SoCs) are marvels of integration, embedding processors, memory, I/O, and various other functionalities into a single chip. This consolidation brings vast benefits in terms of performance and power efficiency but also introduces a myriad of security challenges that must be navigated carefully.</p><p dir="ltr">One of the primary challenges is the increased attack surface. The very features that make SoCs powerful—connectivity, configurability, and smart functionalities—also make them more vulnerable to attacks. With more lines of code, there are more potential bugs or backdoors. With more interfaces, there are more points of entry for malicious actors.</p><p dir="ltr">Additionally, as SoCs become more complex, ensuring that every component is secure from design to decommission becomes harder. This complexity often results in a greater risk of side-channel attacks, where an attacker could infer sensitive information from power consumption, electromagnetic emissions, or even computation time.</p><p dir="ltr">Supply chain security presents another significant challenge. SoCs are global products, with materials and components sourced from and passing through multiple vendors and countries. Each step in this chain presents an opportunity for malicious actors to introduce vulnerabilities.</p><p dir="ltr">Moreover, the need for speed in markets means that security can sometimes be an afterthought, leading to vulnerabilities in the rush to get products to market. As a result, rigorous security testing and validation procedures are essential but can be overlooked or undervalued.</p><p dir="ltr">In dealing with these challenges, the industry has turned to a variety of mitigation strategies. One key approach is the concept of 'security by design', where security measures are considered at every step of the SoC design and manufacturing process. This approach includes using hardware-based security features, like those provided by secure enclaves and trusted execution environments, which can isolate sensitive operations from the main processor to protect them from software attacks.</p><p dir="ltr">Another strategy is regular security audits and updates. Even after an SoC is deployed, its security posture must be maintained and updated in response to new threats—much like how software is regularly patched.</p><p dir="ltr">Finally, leveraging advanced fabrication techniques and materials can reduce the physical vulnerabilities of SoCs, making them harder to tamper with or reverse engineer.</p><p dir="ltr">Navigating these challenges is a complex task, but it is one that is crucial for maintaining trust in technology. In the next section, we discuss how these strategies are put into practice, keeping SoCs secure against an ever-evolving array of threats.</p><h1 dir="ltr">Building Defenses: Mitigation Strategies for SoC Security</h1><p dir="ltr">To confront the security challenges in SoC designs, the semiconductor industry has developed a suite of mitigation strategies that bolster the defenses of these complex systems.</p><ul><li dir="ltr"><p dir="ltr"><strong>Secure Architectural Design:</strong> The first line of defense is secure architectural design. This includes the incorporation of hardware-based security features such as secure boot, trusted execution environments, and secure key storage. By integrating these elements at the design phase, SoCs are better equipped to handle unauthorized access attempts and ensure that critical operations remain uncompromised.</p></li><li dir="ltr"><p dir="ltr"><strong>Layered Security Approach:</strong> A layered security approach is critical for protecting against a wide range of potential attacks. This involves implementing multiple security layers that function together to protect the system. If one layer is breached, the subsequent layers provide an additional level of defense, making it more difficult for an attack to be successful.</p></li><li dir="ltr"><p dir="ltr"><strong>Dynamic Security Measures:&nbsp;</strong>Dynamic security measures, such as runtime monitoring and anomaly detection, are also key. These systems can detect and respond to suspicious behavior in real-time, providing a responsive layer of security that adapts to current threats.</p></li><li dir="ltr"><p dir="ltr"><strong>Encryption and Cryptography:&nbsp;</strong>Strong encryption and cryptography are indispensable in SoC security, safeguarding data both at rest and in transit. Advanced encryption standards and cryptographic algorithms are implemented to ensure that even if data is intercepted, it remains indecipherable without the appropriate keys.</p></li><li dir="ltr"><p dir="ltr"><strong>Regular Security Audits and Firmware Updates:&nbsp;</strong>Ongoing security audits and regular firmware updates ensure that security measures remain up-to-date in the face of evolving threats. This strategy includes the establishment of secure update mechanisms that prevent the installation of malicious firmware.</p></li><li dir="ltr"><p dir="ltr"><strong>Supply Chain Integrity:&nbsp;</strong>Protecting the integrity of the supply chain is another critical strategy. This involves measures such as secure manufacturing practices, tamper-evident packaging, and traceability of components to ensure that SoCs are not compromised at any point from fabrication to delivery.</p></li><li dir="ltr"><p dir="ltr"><strong>User Education and Access Control:</strong> Lastly, user education and strict access control policies are necessary to prevent inadvertent breaches. Users need to be aware of the security features of their devices and how to use them properly, while access control ensures that only authorized individuals can make changes to the system configuration or access sensitive information.</p></li></ul><p dir="ltr">By implementing these strategies, SoC designers and manufacturers can create more secure systems that are resilient in the face of both current and emerging security threats. However, the fast pace of technological change means that vigilance and continuous improvement are mandatory. Security is not a one-time achievement but an ongoing process of adaptation and reinforcement.</p><h1 dir="ltr">Conclusion</h1><p dir="ltr">Silicon-level security stands as a critical pillar in our digital era. Through exploring secure interface technologies, such as Root of Trust and Cryptography IP, we recognize their indispensable role in safeguarding modern SoCs. As we confront security challenges—from side-channel attacks to complex interface vulnerabilities—the need for a robust, multi-layered defense strategy becomes clear.</p><p dir="ltr">Securing silicon goes beyond a single solution—it's a continuous battle, balancing innovation with vigilance. As technology advances, so must our security strategies, integrating state-of-the-art features and adopting a culture of security-first in design and deployment.</p><p dir="ltr">The future hinges on our collective effort to ensure the trustworthiness of our digital foundations. In fostering this trust, the commitment to silicon-level security must remain unwavering, uniting stakeholders across the industry to fortify our digital landscape.</p><h1 dir="ltr">Synopsys' Impact on SoC Security</h1><p dir="ltr">In the realm of SoC security, <a href="https://www.synopsys.com/" target="_blank" rel="noopener noreferrer">Synopsys</a> offers critical technologies that sharpen the defenses of silicon interfaces. Their AI and Machine Learning tools not only expedite chip design but also imbue systems with smart threat detection, essential for anticipatory security. Their cloud solutions provide secure integrations essential for IoT devices dependent on cloud data management.</p><p dir="ltr">Their Design Technology Co-Optimization (DTCO) propels the creation of SoCs resistant to advanced threats by exploring new transistor architectures. Synopsys' emphasis on early-stage security integration through DevSecOps, alongside energy-efficient design practices, further strengthens SoCs against exploits like power analysis attacks.</p><p dir="ltr">Synopsys also pioneers in fortifying system integrity with its Multi-Die System solutions, safeguarding sensitive operations within SoCs. Their vigilance extends to software security, ensuring open-source components and software supply chains are robust against vulnerabilities from inception.</p><p dir="ltr">Through innovation and a strategic approach to security, Synopsys plays a pivotal role in the evolution of more secure digital infrastructures, demonstrating a comprehensive commitment to the advancement of SoC security.</p><h1 dir="ltr" id="isPasted">References</h1><p dir="ltr">[1] Dana Neustadter, Hezi Saar, Michael Posner, ‘How Security for SoC Interfaces Enhances Data Protection’, Synopsis, 4 Oct. 2022, [Online], Available from:&nbsp;<a href="https://www.synopsys.com/blogs/chip-design/soc-interfaces-security-enhance-data-protection.html" target="_blank">https://www.synopsys.com/blogs/chip-design/soc-interfaces-security-enhance-data-protection.html</a></p><p dir="ltr">[2] Marco Ciaffi, John Min, ‘Building security into an AI SoC using CPU features with extensions’, Embedded, 12 Apr. 2021, [Online], Available from:&nbsp;<a href="https://www.embedded.com/building-security-into-an-ai-soc-using-cpu-features-with-extensions/" target="_blank">https://www.embedded.com/building-security-into-an-ai-soc-using-cpu-features-with-extensions/</a></p><p dir="ltr">[3] Ruud Derwig, Nicole Fern, ‘Techniques for Ensuring Security in Processor-based SoCs’, Synopsis, [Online], Available from: <a href="https://www.synopsys.com/designware-ip/technical-bulletin/ensuring-security-processor-socs.html" target="_blank">https://www.synopsys.com/designware-ip/technical-bulletin/ensuring-security-processor-socs.html</a></p><p dir="ltr">[4] Landry J., ‘What Is Hardware Root of Trust?’, Dell, 22 July 2022, [Online], Available from: <a href="https://www.dell.com/en-us/blog/hardware-root-trust/" target="_blank" rel="noopener noreferrer">https://www.dell.com/en-us/blog/hardware-root-trust/</a></p>

Silicon-level security is crucial in protecting the hardware of computing devices, serving as a foundational layer for data integrity and system reliability in our increasingly digital world.


Silicon-Level Security: A Deep Dive into Secure Interfaces

<p dir="ltr" id="isPasted">Moore’s Law and distributed cloud computing have enabled artificial intelligence (AI) and machine learning (ML) applications to effectively overcome Von Neumann bottlenecks that once limited data throughput on conventional systems. With enormous amounts of targeted compute power and memory available in the cloud, AI/ML training and inference models continue to increase in both size and sophistication.</p><p dir="ltr">However, cloud-based data centers have created a new set of challenges for AI applications at the edge such as latency, power, and security. Untethering edge AI from the cloud helps address some of these issues—and creates opportunities for the semiconductor industry to design new products with smarter and more independent sensors, devices, and systems.</p><p dir="ltr">For example, autonomous vehicles leverage cloud-free, edge AI learning at high speeds to continuously update and define safety parameters that make it easier for onboard systems to detect anomalous structural vibrations and engine sounds.</p><p dir="ltr">Cloud-free edge AI also enables gesture control with faster response times, allowing doctors and therapists to help people with disabilities interact with sophisticated robotic assistance devices. In the future, field hospitals in disaster zones can deploy medical robots with advanced edge AI capabilities, even if connectivity is limited.</p><p dir="ltr">Lastly, smart farms in remote areas depend on cloud-free AI to help lower food prices by efficiently increasing crop yields with intelligent soil sensors, irrigation systems, and autonomous drones.</p><h2 dir="ltr">The human brain shows us the way</h2><p dir="ltr">Untethering edge AI from the cloud is an important first step to enabling smarter endpoints. However, the semiconductor industry must also acknowledge the inefficiencies of simply scaling down AI hardware at the edge. Indeed, advanced edge applications are already hitting the limits of conventional AI silicon and standard learning models.</p><p dir="ltr">Many chips used in edge applications today are still general-purpose processors such as GPUs that consume approximately 1,000X more power than purpose-built edge silicon. Although some smart devices are equipped with low-power digital signal processors (DSPs), these single-purpose chips typically lack advanced learning and analytics capabilities.</p><p dir="ltr">In recent years, neuromorphic computing has unlocked more efficient architectures and learning models such as incremental and one-shot learning for edge AI. Smart edge silicon that follows the principles of essential AI—doing more with less—now supports a new generation of advanced multimodal and autonomous use cases with independent learning and inference capabilities, faster response times, and a lower power budget.</p><p dir="ltr">These include self-driving cars that personalize cabin settings for individual drivers, automated factories and warehouses, advanced speech and facial recognition applications, and robots that use sophisticated sensors to see, hear, smell, touch, and taste.&nbsp;</p><h2 dir="ltr">The future’s not only bright, it’s essential</h2><p dir="ltr">Edge AI presents both a challenge and opportunity for the semiconductor industry to look well beyond cutting the cord to the cloud and simply scaling down conventional chip architectures. Neuromorphic edge AI silicon is already enabling people to seamlessly interact with smarter devices that independently learn new skills, intelligently anticipate requests, and instantly deliver services.</p><p dir="ltr">Specific strategies to unlocking the full potential of edge AI will undoubtedly vary, which is why it is important to explore a future in which semiconductor companies play a collaborative role in helping to design and implement new neuromorphic architectures.</p><h2 dir="ltr">Edge AI: The Cloud-Free Future is Now Whitepaper</h2><p dir="ltr">Brainchip and the Global Semiconductor Alliance (GSA) have jointly published a white paper titled “Edge AI: The Cloud-Free Future is Now” that discusses:</p><ul><li dir="ltr"><p dir="ltr">Untethering edge AI from the cloud</p></li><li dir="ltr"><p dir="ltr">Overcoming the limits of conventional AI silicon and learning methodologies</p></li><li dir="ltr"><p dir="ltr">Unlocking new architectures and learning models with neuromorphic computing</p></li><li dir="ltr"><p dir="ltr">Supporting autonomous edge AI applications</p></li></ul><p dir="ltr">“We are excited to support the publication of this white paper as it is a call to our industry to work together to address the unique learning and performance requirements of edge AI,” said Brainchip Marketing and Strategy Advisor, Jerome Nadel. “We hope it provokes conversation and collaboration—and ultimately enables effective edge compute to be universally deployable across real world applications such as connected cars, consumer electronics, and industrial IoT.”</p><p dir="ltr"><a href="https://brainchip.com/#whitepaper" target="_blank" rel="noopener noreferrer" class="button-main-action">Click here to read the full white paper.</a></p><h2 dir="ltr">Building collective knowledge</h2><p dir="ltr">Over the next few months, as part of our new series spotlighting Neuromorphic computing, we will share content that explores how neuromorphic edge silicon can mimic the human brain to analyze only essential sensor inputs at the point of acquisition. Specifically, we’ll take a closer look at the four primary design principles of neuromorphic edge silicon, how to efficiently scale and optimize on-chip memory, as well as strategies for leveraging inference and one-shot learning. We’ll also discuss real-world edge AI use cases, including automotive edge learning at high speeds, wine tasting, object detection and classification, and keyword spotting.</p><p dir="ltr">Follow the <a href="https://www.wevolver.com/profile/brainchip" target="_blank" rel="noopener noreferrer">BrainChip</a> Wevolver profile to stay-up-to date.&nbsp;</p>

This is the first article in a new series from BrainChip highlighting their vision and technology. 


Edge AI: Why the cloud-free future is now

<p dir="ltr" id="isPasted"><em>In a recent episode of the <a href="https://www.youtube.com/@BrainChipInc" target="_blank">BrainChip podcas</a>t, Rob Telson, the Vice President of Sales and Marketing at BrainChip, spoke to Michael Azoff, an industry analyst with a focus on AI at the edge. He has 18 years of experience as an analyst and has previously worked hands-on with neural networks. We highlight some of the key discussion points below but encourage you to listen to the </em><a href="https://www.youtube.com/watch?v=xsIWZXlin7I&ab_channel=BrainChipInc" target="_blank"><em>whole episode here.</em></a></p><h2 dir="ltr">A Dive into the Past and Present</h2><p dir="ltr">During the podcast, Azoff recounted his extensive experience with AI, which began in the 1990s. He reminisced about the early days of neuromorphic computing and how it was just taking root. As the conversation progressed, he highlighted the significant milestones, emphasizing the advancements in training neuromorphic architecture-based neural networks. He also acknowledged BrainChip's notable ascent in recent years, pointing to it as evidence of the growing momentum in the neuromorphic field.</p><h3>Neuromorphic's Niche in Edge AI</h3><p dir="ltr">Azoff's recent contributions to Gigaom's reports have centered on Edge AI, a field that emphasizes inferencing at the edge. In his analysis, the Edge AI domain bifurcates into three distinct categories: ultra-low power, low power, and automotive. Intriguingly, neuromorphic architectures have carved a niche for themselves in the ultra-low power segment. Their inherent efficiency and minimal power consumption make them a natural fit for this category.</p><h3>Applications: From Surveillance to Automotive</h3><p dir="ltr">Neuromorphic architectures excel in scenarios characterized by sparse data. For instance, in visual surveillance, where a static scene is occasionally interrupted by noteworthy events, these architectures prove invaluable. Their design allows them to operate in close proximity to sensors, processing data in real-time. The applications are diverse: smart doorbells, earbuds, industrial fault detection, and even monitoring crowd density at train stations. While vision and audio remain the primary focus areas, the potential applications are vast.</p><p dir="ltr">In the automotive sector, a distinction emerges between in-cabin applications and driving functionalities. While the latter, especially autonomous driving, demands robust processing, neuromorphic computing finds its place in the former. In-cabin applications, ranging from infotainment to human-machine communication, can benefit immensely from neuromorphic's capabilities.</p><h2>Consumer Electronics: The Next Frontier</h2><p dir="ltr">As neuromorphic technology matures, its integration into consumer products is anticipated to grow. The allure of low power consumption cannot be overstated. For application developers, this presents a golden opportunity to innovate, pushing the boundaries of what's possible. In a decade, we might witness a paradigm shift where handheld devices and consumer electronics come equipped with neuromorphic processors, either as accelerators or integral components of the system's ecosystem.</p><h3>AI's Societal Impact and the Dream of Understanding the Human Brain</h3><p dir="ltr">Beyond applications, the podcast touched upon a profound topic: the beneficial impact of AI on society. AI's prowess lies in its ability to automate tasks that surpass human capabilities, especially when scale and complexity come into play. The horizon is vast, with the ultimate goal being the enhancement of AI architectures' intelligence, inching closer to the elusive general artificial intelligence.</p><p>Azoff's vision for an AI superpower is both ambitious and profound: to unravel the mysteries of the human brain. If AI can aid in understanding our brain's intricacies, the implications for neuroscience, psychology, and AI itself could be groundbreaking.</p><h3>In Conclusion</h3><p>This episode of BrainChip's podcast offers a panoramic view of neuromorphic computing's past, present, and future. As we stand on the cusp of AI's next revolution, neuromorphic computing emerges as a beacon, guiding the way forward. The journey is just beginning, and the possibilities are boundless.</p><p><a href="https://www.wevolver.com/profile/brainchip" target="_blank">&nbsp;Stay up to date with BrainChip's new content here.</a></p>

In this episode, Rob Telson speaks with Industry Expert, Michael Azoff, about neuromorphic computing in the AI space including edge AI, neural network architectures, sensory-based applications and more.

BrainChip Podcast: Exploring Neuromorphic Computing with Industry Expert Michael Azoff

<p dir="ltr" id="isPasted"><em>In a recent episode of the <a href="https://www.youtube.com/@BrainChipInc" target="_blank" rel="noopener noreferrer">BrainChip podcas</a>t, a select group of the company's top executives gathered to discuss the future trajectory of artificial intelligence (AI) and the pivotal role BrainChip is set to play in this dynamic landscape. We highlight some of the key discussion points below but encourage you to listen to the&nbsp;</em><a href="https://www.youtube.com/watch?v=zUA9Vdd6cE8&t=2s&ab_channel=BrainChipInc" target="_blank"><em>whole episode here.</em></a></p><h2 dir="ltr">Leadership with Vision</h2><p>Rob Telson Vice President, Ecosystem and Partnerships, is the driving force behind BrainChip's global outreach. With a keen understanding of market dynamics and a vision for BrainChip's expansive global presence, Tolson has been instrumental in positioning the company as a leader in the AI industry.</p><p dir="ltr">Peter Van der Made, <span lang="EN-AU">Executive Director, CTO, and Founder</span> of BrainChip, brings a wealth of experience and a visionary approach to the table. His emphasis on the importance of the company's Advanced Research Center in Perth showcases his commitment to pioneering the next wave of AI innovations. Vandermade's insights into the potential of Akida, BrainChip's flagship technology, highlight his forward-thinking approach to AI's future.</p><p>Anil Mankar, the Vice President of Product Development and also a Co-founder, offers a deep dive into the technical intricacies of BrainChip's operations. His insights into the production process, from chip manufacturing to rigorous testing, provide a glimpse into the meticulous steps BrainChip takes to ensure top-tier functionality and performance.</p><p>Lastly, Ken Scarance, the Chief Financial Officer of BrainChip, sheds light on the company's financial endeavours. His discussions on Brain Chips strategic financial initiatives, including capital raising agreements and efforts to bolster its presence in the U.S. capital markets, underscore the company's ambitions for growth and market dominance.</p><h2 dir="ltr"><strong>BrainChip's Evolution: A Glimpse into the Company's Global Strategy&nbsp;</strong></h2><p>To kick off the episode, the team outlines the company’s history, vision and future plans. BrainChip's commitment to being a global leader in the AI industry is evident in its expansive operational presence. With hubs in California, Perth, France, and India, the company has strategically positioned itself in key tech-centric locations. This global footprint not only facilitates diverse collaborations but also ensures that BrainChip remains at the pulse of AI advancements worldwide.</p><h3 dir="ltr">From Research to Production: A Strategic Pivot</h3><p dir="ltr">Historically, BrainChip has been synonymous with cutting-edge research in neuromorphic computing. Their dedication to pushing the boundaries of AI has positioned them as pioneers in the field. However, recognizing the vast commercial potential of their innovations and the industry's shifting dynamics, BrainChip is undergoing a transformation.</p><p dir="ltr">Tolson emphasised this transition in the podcast, noting the company's pivot from being primarily research-driven to adopting a production-centric approach. For engineers, this shift signifies BrainChip's intent to translate their groundbreaking research into tangible, market-ready solutions.</p><h3 dir="ltr">Engaging the Tech Community</h3><p dir="ltr">Understanding the importance of effective communication in the tech world, especially among engineers and developers, Tolson highlighted BrainChip's efforts to foster engagement. The company's podcast series, for instance, is more than just a marketing tool. It's a platform for knowledge sharing, offering insights into Brain Chips offerings, their vision for AI's future, and the technical intricacies that make their solutions stand out. Stay up to date with BrainChip activities by following their <a href="https://www.wevolver.com/profile/brainchip" target="_blank">Wevolver profile.&nbsp;</a></p><h3 dir="ltr">The Advanced Research Center: A Beacon of Innovation</h3><p dir="ltr">Located in Perth, Australia the Advanced Research Center is BrainChip's crown jewel. It's not just a research facility; it's a testament to the company's dedication to pushing the boundaries of what's possible in AI. While many in the industry focus on refining existing deep learning models, BrainChip's centre is already looking beyond, exploring the next frontier of AI innovations.</p><h3 dir="ltr">Akida: The Future of AI Technology</h3><p dir="ltr">Vandermade's enthusiasm was palpable when discussing <a href="https://brainchip.com/akida-generations/" target="_blank">Akida</a>, BrainChip's flagship technology. Akida is not just another chip in the market; it embodies BrainChip's vision for the future of AI. Two of its standout features are its low energy consumption and its on-chip learning capabilities.</p><p dir="ltr">For engineers and tech enthusiasts, these features are significant. The low energy consumption means that Akida is not only efficient but also environmentally conscious, addressing a growing concern in today's tech-driven world. On the other hand, on-chip learning capabilities represent a leap in AI technology, allowing for faster, more efficient processing without the need for constant back-and-forth with centralised data centres.</p><p dir="ltr">In Vandermade's view, Akida is set to redefine the AI industry. Its unique features position it not just as an innovative product but as a transformative solution that could shape the way we think about and implement AI in various applications.</p><p dir="ltr"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjk3MjA4NDI5MTg2LWFraWRhLmpwZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 300px;" class="fr-fic fr-dib"></p><h3 dir="ltr">Financial Initiatives and Market Presence</h3><p dir="ltr">To wrap up the podcast, Ken Scarance, BrainChip's Chief Financial Officer, touched upon the company's recent financial endeavours. He highlighted the company's agreement with LDA, aimed at raising capital, and BrainChip's strategic move to enhance its presence in the U.S. capital markets. Additionally, the company is bolstering its investor relations strategy, aiming to foster better communication with stakeholders and educate the market about BrainChips groundbreaking offerings.</p><h3 dir="ltr">Conclusion</h3><p dir="ltr">As BrainChip continues to make strides in the AI domain, the company remains committed to keeping its audience informed and engaged. With a series of events lined up to showcase Akida's capabilities and an unwavering focus on innovation, BrainChip is undoubtedly poised to redefine the boundaries of AI.<a href="https://www.wevolver.com/profile/brainchip" target="_blank" rel="noopener noreferrer">&nbsp;Stay up to date with BrainChip's new content here.</a></p>

BrainChip's leadership shares details on the company's development of the Akida chip, future prospects, and more in this informative podcast. 

BrainChip Podcast Leadership Roundtable: These are Exciting Times

Article #5 of Spotlight on Innovations in Edge Computing and Machine Learning: A computer vision project that monitors vehicle traffic in real-time using video inferencing performed on the Brainchip Akida Development Kit.

edge processors

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