<h1 id="isPasted">2023 Edge AI Technology Report. Chapter IV: Hardware &amp; Software Selection</h1><p dir="ltr" id="isPasted">Edge AI, empowered by the recent advancements in Artificial Intelligence, is driving significant shifts in today&#39;s technology landscape. By enabling computation near the data source, Edge AI enhances responsiveness, boosts security and privacy, promotes scalability, enables distributed computing, and improves cost efficiency.</p><p dir="ltr">Wevolver has partnered with industry experts, researchers, and tech providers to create a detailed report on the current state of Edge AI. This document covers its technical aspects, applications, challenges, and future trends. It merges practical and technical insights from industry professionals, helping readers understand and navigate the evolving Edge AI landscape.</p><p dir="ltr"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjg4NDg0MjA0ODUyLUVkZ2UgQUkgRG93bmxvYWQgYmFubmVyICgyKS5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib"></p><h2 dir="ltr"><span class="fr-video fr-deletable fr-fvc fr-dvb fr-draggable" contenteditable="false" draggable="true"><iframe width="540" height="335" src="https://c8056756.sibforms.com/serve/MUIFABFML2jk7knWjihwfmaWcFVV6GvM3WWUP6qryWvjht4DWOrppyQ7-VLwYu9wbjkQCCMxaWS9vmneqio8zYAai1OKHh0-OA61OuFt5xdYgRxECqvgNTAnjjob8456fOSNTOOCe8YLBqs4fiLh0kTJSsBgWCDDvnFUMZSuafNiO9Cbhh5H7fBa4doE8a4CzpRXWMOtAOLUtRmq" 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></h2><hr><h2 dir="ltr">Introduction</h2><p>When selecting and implementing Edge AI systems, it&#39;s important to consider the limitations of edge devices and their compatibility with other hardware and software. Edge devices like sensors and smartphones often have limited processing power, storage, and battery life. These limitations can negatively affect the performance of Edge AI systems, as they may require significant resources to perform complex tasks, such as image recognition and natural language processing. To overcome these limitations, AI models must be optimized to work with limited resources, and specialized hardware such as field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), and GPUs can accelerate computation at the edge.</p><p><span class="fr-img-caption fr-fic fr-dib" style="width: 302px;"><span class="fr-img-wrap"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjg3ODg3MjI2OTA1LW1pY3JvY2hpcC5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib"><span class="fr-inner" spellcheck="false">A field-programmable gate array (FPGA). Image credit: Microchip.</span></span></span></p><p>Edge AI systems are designed to operate for extended periods, so the hardware selected should be capable of supporting future upgrades. It should be designed to accommodate new technologies and features that may be required in the future. Additionally, compatibility is a critical consideration when selecting software for Edge AI systems. Because Edge AI systems often require integration with existing infrastructure and hardware, it&#39;s important to select software that is compatible with a wide range of devices and platforms. This includes both hardware compatibility, such as compatibility with different types of sensors and processors, and software compatibility, such as compatibility with different operating systems and programming languages.</p><h3>Hardware Considerations</h3><p>Ensuring that the chosen hardware meets the requirements of Edge AI systems involves considering various factors. One key consideration is processing power, as Edge AI applications require hardware capable of processing data quickly and accurately. The hardware&#39;s computing power and clock speed play crucial roles in achieving real-time processing, which is essential in Edge AI systems. Another critical consideration is the amount and type of memory available in the hardware. Edge AI applications necessitate sufficient memory to store and process large volumes of data, with a focus on fast and real-time processing capabilities. There is a race to have the highest number of TOPS (tera-operations per second) in the hardware industry, but it&#39;s not a one-to-one comparison.</p><blockquote><p>&ldquo;The memory system handling, DMA configuration, software compression of weights, intelligence, and decompression all matter significantly. Focusing only on the box numbers is similar to focusing solely on the megapixel or gigapixel of a phone&#39;s camera without considering other factors. This tunnel vision is an error for the industry to make.&rdquo;&nbsp;<br>- <span style="background-color: transparent;">Rahul Venkatram, Senior Product Manager, Machine Learning, Arm</span></p></blockquote><p>Power consumption is another vital aspect, particularly for devices with limited power resources like IoT devices. Hardware optimization for low power consumption becomes crucial to ensure prolonged device operation without frequent battery replacements or recharging. Techniques such as utilizing low-power chips, hardware accelerators, and intelligent power management systems contribute to achieving low power consumption in Edge AI systems. In a conversation with Henrik Flodell, Director of Product Marketing at Alif Semiconductor, he emphasized that &ldquo;reducing power consumption is a key parameter users need to consider when they pick suitable devices for edge systems that will use ML.&rdquo;&nbsp;</p><p>The Autonomous Intelligent Power Management (aiPM&trade;) technology from Alif is a vital solution for power consumption. It helps with selectively powering on the sections of an SoC that are needed when they are needed, and it powers them off when they are not, based on instantaneous processing demand and use case. It makes use of dual-processing regions within the MCU to speed up machine learning. The always-on, high-efficiency region continuously senses the environment, while the high-performance region wakes up when needed so it can execute heavy workloads and then return to sleep. This approach allows powerful devices to behave like low-power MCUs when necessary, which enables longer performance of smart IoT devices on smaller batteries.</p><blockquote><p>&ldquo;Systems that have to rely on CPU-bound processing tend to be power hungry, as the inference operations would take longer to finish, and the controller would run at 100% for much longer periods of time. Well-designed modern platforms offload the ML processing on accelerators designed for such workloads and can, therefore, finish faster and go to sleep sooner.&rdquo;&nbsp;<br><span style="background-color: transparent;">- Henrik Flodell, Direct of Product Marketing, Alif Semiconductor.</span></p></blockquote><p>Seamless connectivity is also essential for Edge AI systems, requiring hardware that enables smooth data exchange with other devices and cloud-based platforms. The hardware should offer various connectivity options, including Wi-Fi, Bluetooth, and cellular networks, facilitating efficient communication and data processing with other devices. This connectivity ensures that data is processed and analyzed quickly and efficiently, contributing to the overall performance of the Edge AI system.</p><p><span class="fr-img-caption fr-fic fr-dib" style="width: 302px;"><span class="fr-img-wrap"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjg3ODg3MjkzMjM2LWFyZHVpbm8ucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 300px;" class="fr-fic fr-dib"><span class="fr-inner" spellcheck="false">The Edge Control from Arduino is a remote monitoring and control solution optimized for outdoor environments that enables users to collect real-time data from smart sensors and leverage AI at the edge. Image credit: Arduino.</span></span></span></p><h3>Software Considerations</h3><p>When it comes to Edge AI systems, selecting software with specific characteristics is crucial for optimal operation. Analyzing various factors ensures that the chosen software meets the requirements of the Edge AI system. Compatibility stands as a significant consideration in software selection. The software should run efficiently on the hardware, enabling real-time data processing. Moreover, compatibility with other software components used in the system, such as operating systems, libraries, and frameworks, ensures seamless integration and functionality.</p><p>Scalability is another vital aspect to consider in Edge AI system design. Given that Edge AI systems often handle large amounts of data, the software must be capable of handling the real-time processing and analysis demands associated with such data. Scalable software guarantees the system can accommodate increasing data volumes, processing requirements, and user requests without compromising performance.</p><p>The accuracy of the software employed in Edge AI systems holds great importance. These systems heavily rely on accurate data analysis and processing to provide meaningful insights and support decision-making. Therefore, the software must exhibit high accuracy and reliability in analyzing and processing data.</p><p>Interpretability, or the software&#39;s ability to explain its results comprehensibly, plays a crucial role in Edge AI system design. Interpretability allows users to understand the system&#39;s decision-making process and provides insights into data analysis. This aspect becomes particularly significant in applications where the decisions made by the Edge AI system have substantial implications, such as healthcare and finance. The software used in Edge AI systems should prioritize interpretability, presenting results in a clear and understandable manner.</p><h3>Integration Considerations</h3><p>Integrating Edge AI with other systems is essential for seamless and efficient operations. As Edge AI systems generate vast amounts of data that require processing, analysis, and sharing with other systems, selecting communication protocols becomes imperative. Different protocols are used by Edge AI systems to communicate with other systems, and it is crucial to choose a protocol that is compatible with the integrated systems.</p><p>Several commonly used protocols in Edge AI system integration include MQTT, CoAP, and HTTP. MQTT is a lightweight protocol widely utilized in IoT systems due to its low overhead and power consumption. CoAP, on the other hand, is designed specifically for resource-constrained networks, making it suitable for Edge AI systems. HTTP, a widely used protocol in web-based applications, is also suitable for Edge AI systems that communicate with web-based applications. The choice of communication protocol should align with the requirements of the Edge AI system and the systems being integrated.</p><p><span class="fr-img-caption fr-fic fr-dib" style="width: 302px;"><span class="fr-img-wrap"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjg3ODg3MzU4NTUyLUlNQUdFUyAyLTA0LmpwZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 300px;" class="fr-fic fr-dib"><span class="fr-inner" spellcheck="false">Comparison of protocols in terms of power consumption and resource requirement.</span></span></span></p><p>In addition to communication protocols, data formats are another critical consideration when integrating Edge AI with other systems. Edge AI systems generate data in various formats, and it is vital to ensure compatibility between the data generated by the Edge AI system and the systems it interacts with. Data formats must be aligned and properly transformed or translated to facilitate seamless data exchange and interoperability between the systems.</p><p>Global technology provider, Arm, has approached integration from a holistic thought process about where and how to deploy processing. David Maidment, Senior Director, Secure Device Ecosystem at Arm, explained their approach as follows: &ldquo;We&#39;ve landscaped our approach in two directions. The first is scaling down traditional cloud-native workloads, such as microservers. The second direction is taking IoT and embedded devices and making them connected, updateable, and secure. We&#39;re seeing these two trends converge, with higher-end, higher-power devices coming down in cost and power, and lower-end, lower-power devices being pushed up in performance capabilities.&rdquo;</p><p>This results in two distinct software ecosystems. The first is the traditional cloud software ecosystem, with portable workloads that can run on servers with offload and acceleration. The second is a more traditional Linux box that was initially designed for a single function, like a router or gateway but is now being developed as a connected general-purpose box with capabilities that can change during its lifetime through new applications and AI models.</p><h3>Security Considerations</h3><p>Ensuring security is a critical factor when integrating Edge AI with other systems. Since Edge AI systems generate vast amounts of data, including sensitive information, protecting it from unauthorized access becomes imperative. Secure communication protocols such as SSL/TLS and SSH should be employed to establish secure channels for data transmission between the Edge AI system and other systems. These protocols encrypt the data, preventing eavesdropping and tampering during transit.</p><p>To reinforce security, access controls and authentication mechanisms must be implemented. This ensures that only authorized users can access the data generated by the Edge AI system, safeguarding it from unauthorized individuals. Secure software development practices, such as regular code reviews and vulnerability assessments, should also be followed to identify and mitigate potential security risks in the integrated systems.</p><p>Encryption is pivotal in securing Edge AI systems and should not be overlooked. By converting sensitive data into an encoded form, encryption ensures that even if the data is compromised, it remains unintelligible to unauthorized entities. Given that Edge AI systems often process sensitive data like personal health information, financial records, and valuable intellectual property, encrypting this data is crucial in preventing unauthorized access, theft, or manipulation.</p><p><span class="fr-img-caption fr-fic fr-dib" style="width: 302px;"><span class="fr-img-wrap"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjg3ODg3NDU1ODEyLTA1IGltYWdlLTAxLmpwZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 300px;" class="fr-fic fr-dib"><span class="fr-inner" spellcheck="false">Key usage in a cryptographic system of an IoT device and attack paths.<br></span></span></span></p><p><em id="isPasted">Gain a broader perspective on Edge AI by revisiting our&nbsp;</em><a href="https://www.wevolver.com/article/edge-ai-a-comprehensive-guide-to-its-engineering-principles-and-applications" rel="noopener noreferrer" target="_blank"><em>comprehensive guide</em></a><em>.</em></p><hr><h1 dir="ltr" id="isPasted">The 2023 Edge AI Technology Report</h1><p dir="ltr"><em>The guide to understanding the state of the art in hardware &amp; software in Edge AI.</em></p><p dir="ltr">Click through to read each of the report&#39;s chapters.&nbsp;</p><h2 id="isPasted"><a target="_blank">Report Introduction</a></h2><h3><a href="https://www.wevolver.com/article/2023-edge-ai-technology-report-chapter-i-overview-of-industries-and-application-use-cases" rel="noopener noreferrer" target="_blank">Chapter I: Overview of Industries and Application Use Cases</a></h3><h3 dir="ltr"><a href="https://www.wevolver.com/article/2023-edge-ai-technology-report-chapter-ii-advantages-of-edge-ai" rel="noopener noreferrer" target="_blank">Chapter II: Advantages of Edge AI</a></h3><h3><a href="https://www.wevolver.com/article/2023-edge-ai-technology-report-chapter-iii-edge-ai-platforms" rel="noopener noreferrer" target="_blank">Chapter III: Edge AI Platforms</a></h3><h3>Chapter IV: Hardware and Software Selection</h3><h3><a href="https://www.wevolver.com/article/2023-edge-ai-technology-report-chapter-v-tinyml" rel="noopener noreferrer" target="_blank">Chapter V: Tiny ML</a></h3><h3><a href="https://www.wevolver.com/article/2023-edge-ai-technology-report-chapter-vi-edge-ai-algorithms" rel="noopener noreferrer" target="_blank">Chapter VI: Edge AI Algorithms</a></h3><h3><a href="https://www.wevolver.com/article/2023-edge-ai-technology-report-chapter-vii-sensing-modalities" rel="noopener noreferrer" target="_blank">Chapter VII: Sensing Modalities</a></h3><h3><a href="https://www.wevolver.com/article/2023-edge-ai-technology-report-chapter-viii-case-studies" rel="noopener noreferrer" target="_blank">Chapter VIII: Case Studies</a></h3><h3 dir="ltr"><a href="https://www.wevolver.com/article/2023-edge-ai-technology-report-chapter-ix-challenges-of-edge-ai" rel="noopener noreferrer" target="_blank">Chapter IX: Challenges of Edge AI</a></h3><h3><a href="https://www.wevolver.com/article/2023-edge-ai-technology-report-chapter-x-future-of-edge-ai" rel="noopener noreferrer" target="_blank">Chapter X: The Future of Edge AI and Conclusion</a></h3><p><span class="fr-video fr-deletable fr-fvc fr-dvb fr-draggable" contenteditable="false" draggable="true"><iframe width="540" height="335" src="https://c8056756.sibforms.com/serve/MUIFABFML2jk7knWjihwfmaWcFVV6GvM3WWUP6qryWvjht4DWOrppyQ7-VLwYu9wbjkQCCMxaWS9vmneqio8zYAai1OKHh0-OA61OuFt5xdYgRxECqvgNTAnjjob8456fOSNTOOCe8YLBqs4fiLh0kTJSsBgWCDDvnFUMZSuafNiO9Cbhh5H7fBa4doE8a4CzpRXWMOtAOLUtRmq" 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><hr><h2>The report sponsors.</h2><p><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjg3ODg3NTQ5OTc0LTExICgxKS5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib"></p><p><br></p>

What to Consider When Selecting Your Edge AI Hardware and Software.

2023 Edge AI Technology Report. Chapter IV: Hardware & Software Selection

Introduction

An Overview of the Different Edge AI Platforms.


2023 Edge AI Technology Report. Chapter III: Edge AI Platforms

How Edge AI is Transforming Industries and Application Areas.

2023 Edge AI Technology Report. Chapter I: Overview of Industries & Application Use Cases

<h1 id="isPasted">2023 Edge AI Technology Report.&nbsp;</h1><p dir="ltr">Edge AI, empowered by the recent advancements in Artificial Intelligence, is driving significant shifts in today&#39;s technology landscape. By enabling computation near the data source, Edge AI enhances responsiveness, boosts security and privacy, promotes scalability, enables distributed computing, and improves cost efficiency.</p><p dir="ltr">Wevolver has partnered with industry experts, researchers, and tech providers to create a detailed report on the current state of Edge AI. This document covers its technical aspects, applications, challenges, and future trends. It merges practical and technical insights from industry professionals, helping readers understand and navigate the evolving Edge AI landscape.</p><p dir="ltr"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjg4NTQ4Nzc1NDA1LUVkZ2UgQUkgRG93bmxvYWQgYmFubmVyICgyKS5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib"></p><p dir="ltr"><span class="fr-video fr-deletable fr-fvc fr-dvb fr-draggable fr-active" contenteditable="false" draggable="true"><iframe src="https://c8056756.sibforms.com/serve/MUIFABFML2jk7knWjihwfmaWcFVV6GvM3WWUP6qryWvjht4DWOrppyQ7-VLwYu9wbjkQCCMxaWS9vmneqio8zYAai1OKHh0-OA61OuFt5xdYgRxECqvgNTAnjjob8456fOSNTOOCe8YLBqs4fiLh0kTJSsBgWCDDvnFUMZSuafNiO9Cbhh5H7fBa4doE8a4CzpRXWMOtAOLUtRmq" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block; margin-left: auto; margin-right: auto; max-width: 100%; width: 540px; height: 335px;" 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><hr><h1 id="isPasted">Report Introduction</h1><p dir="ltr">The advent of Artificial Intelligence (AI) over recent years has truly revolutionized our industries and personal lives, offering unprecedented opportunities and capabilities. However, while cloud-based processing and cloud AI took off in the past decade, we have come to experience issues such as latency, bandwidth constraints, and security and privacy concerns, to name a few. That is where the emergence of Edge AI became extremely valuable and transformed the AI landscape.</p><p dir="ltr">Edge AI represents a paradigm shift in AI deployment, bringing computational power closer to the data source. It allows for on-device data processing and enables real-time, context-aware decision-making. Instead of relying on cloud-based processing, Edge AI utilizes edge devices such as sensors, cameras, smartphones, and other compact devices to perform AI computations on the device itself. Such an approach offers multitudes of advantages, including reduced latency, improved bandwidth efficiency, enhanced data privacy, and increased reliability in scenarios with limited or intermittent connectivity.</p><blockquote><p dir="ltr">&ldquo;Even with ubiquitous 5G, connectivity to the cloud isn&rsquo;t guaranteed, and bandwidth isn&rsquo;t assured in every case. The move to AIoT increasingly needs that intelligence and computational power at the edge.&rdquo;<br id="isPasted">- Nandan Nayampally, CMO, Brainchip</p></blockquote><p dir="ltr" id="isPasted">While Cloud AI predominantly performs data processing and analysis in remote servers, Edge AI focuses on enabling AI capabilities directly on the devices. The key distinction here lies in the processing location and the nature of the data being processed. Cloud AI is suitable for processing-intensive applications that can tolerate latency, while Edge AI excels in time-sensitive scenarios where real-time processing is essential. By deploying AI models directly on edge devices, Edge AI minimizes the reliance on cloud connectivity, enabling localized decision-making and response.</p><p dir="ltr">The Edge encompasses the entire spectrum from data centers to IoT endpoints. This includes the data center edge, network edge, embedded edge, and on-prem edge, each with its own use cases. The compute requirements essentially determine where a particular application falls on the spectrum, ranging from data-center edge solutions to small sensors embedded in devices like automobile tires. Vibration-related applications would be positioned towards one end of the spectrum, often implemented on microcontrollers, while more complex video analysis tasks might be closer to the other end, sometimes on more powerful microprocessors.</p><blockquote><p dir="ltr" id="isPasted">&ldquo;Applications are gradually moving towards the edge as these edge platforms enhance their compute power.&rdquo;&nbsp;<br>- <span style="background-color: transparent;">Ian Bratt, Fellow and Senior Director of Technology, Arm</span></p></blockquote><p dir="ltr" id="isPasted">When it comes to Edge AI, the focus is primarily on sensing systems. This includes camera-based systems, audio sensors, and applications like traffic monitoring in smart cities. Edge AI essentially functions as an extensive sensory system, continuously monitoring and interpreting events in the world. In an integrated-technology approach, the collected information can then be sent to the cloud for further processing.</p><p dir="ltr">Edge AI shines in applications where rapid decision-making and immediate response to time-sensitive data are required. For instance, in autonomous driving, Edge AI empowers vehicles to process sensor data onboard and make split-second decisions to ensure safe navigation. Similarly, in healthcare, Edge AI enables real-time patient monitoring, detecting anomalies, and facilitating immediate interventions. The ability to process and analyze data locally empowers healthcare professionals to deliver timely and life-saving interventions.</p><p dir="ltr">Edge AI application areas can be distinguished based on specific requirements such as power sensitivity, size limitations, weight constraints, and heat dissipation. Power sensitivity is a crucial consideration, as edge devices are often low-power devices used in smartphones, wearables, or Internet of Things (IoT) systems. AI models deployed on these devices must be optimized for efficient power consumption to preserve battery life and prolong operational duration.</p><p dir="ltr">Size limitations and weight constraints also play quite a significant role in distinguishing Edge AI application areas. Edge devices are typically compact and portable, making it essential for AI models to be lightweight and space-efficient. This consideration is particularly relevant upon integrating edge devices into drones, robotics, or wearable devices, where size and weight directly impact performance and usability.</p><p dir="ltr">Nevertheless, edge computing presents significant advantages that weren&rsquo;t achievable beforehand. Owning the data, for instance, provides a high level of security, as there is no need for the data to be sent to the cloud, thus mitigating the increasing cybersecurity risks. Edge computing also reduces latency and power usage due to less communication back and forth with the cloud, which is particularly important for constrained devices running on low power. And the advantages don&rsquo;t stop there, as we are seeing more and more interesting developments in real-time performance and decision-making, improved privacy control, and on-device learning, enabling intelligent devices to operate autonomously and adaptively without relying on constant cloud interaction.</p><blockquote><p dir="ltr" id="isPasted">&ldquo;The recent surge in AI has been fueled by a harmonious interplay between cutting-edge algorithms and advanced hardware. As we move forward, the symbiosis of these two elements will become even more crucial, particularly for Edge AI.&rdquo;&nbsp;<br>- Dr. Bram Verhoef, Head of Machine Learning at Axelera AI</p></blockquote><p dir="ltr" id="isPasted">Edge AI holds immense significance in the current and future technology landscape. With decentralized AI processing, improved responsiveness, enhanced privacy and security, cost-efficiency, scalability, and distributed computing, Edge AI is revolutionizing our world as we speak. And with the rapid developments happening constantly, it may be difficult to follow all the new advancements in the field.&nbsp;</p><p dir="ltr" id="isPasted">That is why Wevolver has collaborated with several industry experts, researchers, professors, and leading companies to create a comprehensive report on the current state of Edge AI, exploring its history, cutting-edge applications, and future developments. This report will provide you with practical and technical knowledge to help you understand and navigate the evolving landscape of Edge AI.</p><p dir="ltr">This report would not have been possible without the esteemed contributions and sponsorship of Alif Semiconductor, Arduino, Arm, Axelera AI, BrainChip, Edge Impulse, GreenWaves Technologies, Sparkfun, ST, and Synaptics. Their commitment to objectively sharing knowledge and insights to help inspire innovation and technological evolution aligns perfectly with what Wevolver does and the impact it aims to achieve.</p><p dir="ltr">As the world becomes increasingly connected and data-driven, Edge AI is emerging as a vital technology at the core of this transformation, and we hope this comprehensive report provides all the knowledge and inspiration you need to participate in this technological journey.</p><p dir="ltr"><strong>Samir Jaber</strong><br>Editor-in-Chief</p><p dir="ltr"><em>This text was contributed to by engineers from&nbsp;</em><span style="background-color: transparent;"><em>Arm,&nbsp;</em></span><span style="background-color: transparent;"><em>Axelera AI, and&nbsp;</em></span><span style="background-color: transparent;"><em>BrainChip.</em></span></p><p dir="ltr"><span style="background-color: transparent;"><em><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjg3ODg2MDk4NTA0LUVkZ2UgQUkgRG93bmxvYWQgYmFubmVyICgxKS5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 302px;" class="fr-fic fr-dib"></em></span></p><p><em><span class="fr-video fr-deletable fr-fvc fr-dvb fr-draggable" contenteditable="false" draggable="true"><iframe width="540" height="335" src="https://c8056756.sibforms.com/serve/MUIFABFML2jk7knWjihwfmaWcFVV6GvM3WWUP6qryWvjht4DWOrppyQ7-VLwYu9wbjkQCCMxaWS9vmneqio8zYAai1OKHh0-OA61OuFt5xdYgRxECqvgNTAnjjob8456fOSNTOOCe8YLBqs4fiLh0kTJSsBgWCDDvnFUMZSuafNiO9Cbhh5H7fBa4doE8a4CzpRXWMOtAOLUtRmq" 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></em></p><hr><h2><span style="background-color: transparent;">The 2023 Edge AI Technology Report</span></h2><p dir="ltr"><em>The guide to understanding the state of the art in hardware &amp; software in Edge AI.</em></p><p dir="ltr">Click through to read each of the report&#39;s chapters.&nbsp;</p><h3><a href="https://www.wevolver.com/article/2023-edge-ai-technology-report-chapter-i-overview-of-industries-application-use-cases/draft" rel="noopener noreferrer" target="_blank">Chapter I: Overview of Industries and Application Use Cases</a></h3><h3 dir="ltr"><a href="https://www.wevolver.com/article/2023-edge-ai-technology-report-chapter-ii-advantages-of-edge-ai" rel="noopener noreferrer" target="_blank">Chapter II: Advantages of Edge AI</a></h3><h3><a href="https://www.wevolver.com/article/2023-edge-ai-technology-report-chapter-iii-edge-ai-platforms" rel="noopener noreferrer" target="_blank">Chapter III: Edge AI Platforms</a></h3><h3><a href="https://www.wevolver.com/article/2023-edge-ai-technology-report-chapter-iv-hardware-software-selection" rel="noopener noreferrer" target="_blank">Chapter IV: Hardware and Software Selection</a></h3><h3><a href="https://www.wevolver.com/article/2023-edge-ai-technology-report-chapter-v-tinyml/draft" rel="noopener noreferrer" target="_blank">Chapter V: Tiny ML</a></h3><h3><a href="https://www.wevolver.com/article/2023-edge-ai-technology-report-chapter-vi-edge-ai-algorithms" rel="noopener noreferrer" target="_blank">Chapter VI: Edge AI Algorithms</a></h3><h3><a href="https://www.wevolver.com/article/2023-edge-ai-technology-report-chapter-vii-sensing-modalities" rel="noopener noreferrer" target="_blank">Chapter VII: Sensing Modalities</a></h3><h3><a href="https://www.wevolver.com/article/2023-edge-ai-technology-report-chapter-viii-case-studies" rel="noopener noreferrer" target="_blank">Chapter VIII: Case Studies</a></h3><h3 dir="ltr"><a href="https://www.wevolver.com/article/2023-edge-ai-technology-report-chapter-ix-challenges-of-edge-ai" rel="noopener noreferrer" target="_blank">Chapter IX: Challenges of Edge AI</a></h3><h3><a href="https://www.wevolver.com/article/2023-edge-ai-technology-report-chapter-x-future-of-edge-ai" rel="noopener noreferrer" target="_blank">Chapter X: The Future of Edge AI and Conclusion</a></h3><hr><h2 id="isPasted"><strong>How this report came to be: A collaborative effort</strong></h2><p>This report is the culmination of collaborative efforts among a diverse team of contributors. Initial input was graciously provided by our sponsors and partner, who shared their expert opinions and insightful case studies, creating a strong technical foundation. A dedicated team of writers further expanded these texts under the careful guidance of our Editor-in-Chief. Thank you to our report sponsors and report partner, the tinyML Foundation. &nbsp;</p><p><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjg3ODgxMTAwNjI0LTExICgxKS5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib"></p><h3>About the writers</h3><h3 dir="ltr">Samir Jaber, Editor-in-chief.&nbsp;</h3><p dir="ltr"><a href="https://www.wevolver.com/profile/samir.jaber" rel="noopener noreferrer" target="_blank">Samir Jaber</a> is an SEO &amp; Content Specialist with a background in engineering, nanotechnology, and scientific research. Samir has comprehensive experience working with major engineering and technology companies as a writer, editor, and digital marketing consultant. He is an expert in content management and strategy, particularly in the engineering and tech fields. He is a featured author in 30+ industrial magazines with a focus on IoT, nanotechnology, materials science, engineering, and sustainability. Samir is also an award-winning engineering researcher in the fields of nanofabrication and microfluidics.</p><h3 dir="ltr">Dr. John Soldatos</h3><p dir="ltr"><a href="https://www.wevolver.com/profile/john.soldatos" rel="noopener noreferrer" target="_blank">John Soldatos</a> 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&ndash;2019), and Adjunct Professor at the Carnegie Mellon University, Pittsburgh, PA (2007&ndash;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><h3 dir="ltr">Dr. Miroslav Milovanovic</h3><p dir="ltr"><a href="https://www.wevolver.com/profile/miroslav.milovanovic" rel="noopener noreferrer" target="_blank">Miroslav Milovanovic</a> has a Ph.D. in Computer Science and Electrotechnics and works as an assistant professor at the Faculty of Electronic Engineering. He teaches courses strongly connected with Data Science, the Industrial Internet of Things, Modern Control of Industrial Processes, and Intelligent control.&nbsp;</p><p dir="ltr">Additionally, Dr. Milovanovic is the Chief of the Laboratory for Intelligent Control in the Control Systems Department. His instructional expertise encompasses courses strongly connected with Data Science, the Industrial Internet of Things, Deep Learning, Machine Learning in Python, Modern Control of Industrial Processes, and Intelligent Control. By imparting practical knowledge in these areas, he empowers students to excel in their chosen fields. With over 45 published scientific papers, his research primarily revolves around Data Science, Deep Learning, and their practical applications.</p><h3 dir="ltr">Lydia Husser</h3><p dir="ltr"><a href="https://www.wevolver.com/profile/lydia.husser" rel="noopener noreferrer" target="_blank">Lydia Husser</a> is a technical writer in robotics and a freelance writer captivated by innovative science and engineering. She holds a degree in Computer Science and credentials in electronics technology. Her background includes work in robotics, communications hardware, and developer documentation.&nbsp;</p><p dir="ltr">The cover image of the report was created using generative AI tools. Credit to Muthali Ganesh.</p>

<article id="isPasted"><p dir="ltr"><em>We are thrilled to share the&nbsp;</em><a href="https://www.wevolver.com/article/2024-state-of-edge-ai-report"><em>2024 State of Edge AI Report</em></a><em>. This is Wevolver's second in-depth report on Edge AI, building on our 2023 report which focused on the technological foundations. While our previous report explored the technical landscape, this year's edition takes a deeper look at real-world applications and industry-specific implementations.<br></em></p></article><hr><h1 id="isPasted">2023 Edge AI Technology Report.&nbsp;</h1><p dir="ltr">Edge AI, empowered by the recent advancements in Artificial Intelligence, is driving significant shifts in today's technology landscape. By enabling computation near the data source, Edge AI enhances responsiveness, boosts security and privacy, promotes scalability, enables distributed computing, and improves cost efficiency.</p><p dir="ltr">Wevolver has partnered with industry experts, researchers, and tech providers to create a detailed report on the current state of Edge AI. This document covers its technical aspects, applications, challenges, and future trends. It merges practical and technical insights from industry professionals, helping readers understand and navigate the evolving Edge AI landscape.</p><p dir="ltr"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjg4NTQ4Nzc1NDA1LUVkZ2UgQUkgRG93bmxvYWQgYmFubmVyICgyKS5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib"></p><p dir="ltr"><span class="fr-video fr-deletable fr-fvc fr-dvb fr-draggable" contenteditable="false" draggable="true"><iframe src="https://c8056756.sibforms.com/serve/MUIFABFML2jk7knWjihwfmaWcFVV6GvM3WWUP6qryWvjht4DWOrppyQ7-VLwYu9wbjkQCCMxaWS9vmneqio8zYAai1OKHh0-OA61OuFt5xdYgRxECqvgNTAnjjob8456fOSNTOOCe8YLBqs4fiLh0kTJSsBgWCDDvnFUMZSuafNiO9Cbhh5H7fBa4doE8a4CzpRXWMOtAOLUtRmq" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block; margin-left: auto; margin-right: auto; max-width: 100%; width: 540px; height: 335px;" 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><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span></iframe></span></p><h1 id="isPasted">Report Introduction</h1><p dir="ltr">The advent of Artificial Intelligence (AI) over recent years has truly revolutionized our industries and personal lives, offering unprecedented opportunities and capabilities. However, while cloud-based processing and cloud AI took off in the past decade, we have come to experience issues such as latency, bandwidth constraints, and security and privacy concerns, to name a few. That is where the emergence of Edge AI became extremely valuable and transformed the AI landscape.</p><p dir="ltr">Edge AI represents a paradigm shift in AI deployment, <a href="https://www.wevolver.com/article/2023-edge-ai-technology-report-chapter-ii-advantages-of-edge-ai">bringing computational power closer to the data source</a>. It allows for on-device data processing and enables real-time, context-aware decision-making. Instead of relying on cloud-based processing, Edge AI utilizes <a href="https://www.wevolver.com/article/2023-edge-ai-technology-report-chapter-vii-sensing-modalities">edge devices such as sensors, cameras, smartphones</a>, and other compact devices to perform AI computations on the device itself. Such an approach offers multitudes of advantages, including reduced latency, improved bandwidth efficiency, enhanced data privacy, and increased reliability in scenarios with limited or intermittent connectivity.</p><blockquote><p dir="ltr">“Even with ubiquitous 5G, connectivity to the cloud isn’t guaranteed, and bandwidth isn’t assured in every case. The move to AIoT increasingly needs that intelligence and computational power at the edge.”<br id="isPasted">- Nandan Nayampally, CMO, Brainchip</p></blockquote><p dir="ltr" id="isPasted">While Cloud AI predominantly performs data processing and analysis in remote servers, <a href="https://www.wevolver.com/article/2023-edge-ai-technology-report-chapter-iii-edge-ai-platforms">Edge AI focuses on enabling AI capabilities directly on the devices</a>. The key distinction here lies in the processing location and the nature of the data being processed. Cloud AI is suitable for processing-intensive applications that can tolerate latency, while Edge AI excels in time-sensitive scenarios where real-time processing is essential. By deploying AI models directly on edge devices, Edge AI minimizes the reliance on cloud connectivity, enabling localized decision-making and response.</p><p dir="ltr">The Edge encompasses the entire spectrum from data centers to IoT endpoints. This includes the data center edge, network edge, embedded edge, and on-prem edge, each with its own use cases. The compute requirements essentially determine where a particular application falls on the spectrum, ranging from data-center edge solutions to small sensors embedded in devices like automobile tires. Vibration-related applications would be positioned towards one end of the spectrum, often implemented on microcontrollers, while more complex video analysis tasks might be closer to the other end, sometimes on more powerful microprocessors.</p><blockquote><p dir="ltr" id="isPasted">“Applications are gradually moving towards the edge as these edge platforms enhance their compute power.”&nbsp;<br>- <span style="background-color: transparent;">Ian Bratt, Fellow and Senior Director of Technology, Arm</span></p></blockquote><p dir="ltr" id="isPasted">When it comes to Edge AI, the focus is primarily on sensing systems. This includes camera-based systems, audio sensors, and applications like traffic monitoring in smart cities. Edge AI essentially functions as an extensive sensory system, continuously monitoring and interpreting events in the world. In an integrated-technology approach, the collected information can then be sent to the cloud for further processing.</p><p dir="ltr">Edge AI shines in applications where rapid decision-making and immediate response to time-sensitive data are required. For instance, in autonomous driving, Edge AI empowers vehicles to process sensor data onboard and make split-second decisions to ensure safe navigation. Similarly, in healthcare, Edge AI enables real-time patient monitoring, detecting anomalies, and facilitating immediate interventions. The ability to process and analyze data locally empowers healthcare professionals to deliver timely and life-saving interventions.</p><p dir="ltr">Edge AI application areas can be distinguished based on specific requirements such as power sensitivity, size limitations, weight constraints, and heat dissipation. Power sensitivity is a crucial consideration, as edge devices are often low-power devices used in smartphones, wearables, or Internet of Things (IoT) systems. AI models deployed on these devices must be optimized for efficient power consumption to preserve battery life and prolong operational duration.</p><p dir="ltr">Size limitations and weight constraints also play quite a significant role in distinguishing Edge AI application areas. Edge devices are typically compact and portable, making it essential for AI models to be lightweight and space-efficient. This consideration is particularly relevant upon integrating edge devices into drones, robotics, or wearable devices, where size and weight directly impact performance and usability.</p><p dir="ltr">Nevertheless, edge computing presents <a href="https://www.wevolver.com/article/2023-edge-ai-technology-report-chapter-viii-case-studies">significant advantages that weren’t achievable beforehand.</a> Owning the data, for instance, provides a high level of security, as there is no need for the data to be sent to the cloud, thus mitigating the increasing cybersecurity risks. Edge computing also reduces latency and power usage due to less communication back and forth with the cloud, which is particularly important for constrained devices running on low power. And the advantages don’t stop there, as we are seeing more and more interesting developments in <a href="https://www.wevolver.com/article/2023-edge-ai-technology-report-chapter-ii-advantages-of-edge-ai">real-time performance and decision-making</a>, improved privacy control, and <a href="https://www.wevolver.com/article/2023-edge-ai-technology-report-chapter-vi-edge-ai-algorithms">on-device learning</a>, enabling intelligent devices to operate autonomously and adaptively without relying on constant cloud interaction.</p><blockquote><p dir="ltr" id="isPasted">“The recent surge in AI has been fueled by a harmonious interplay between cutting-edge algorithms and advanced hardware. As we move forward, the symbiosis of these two elements will become even more crucial, particularly for Edge AI.”&nbsp;<br>- Dr. Bram Verhoef, Head of Machine Learning at Axelera AI</p></blockquote><p dir="ltr" id="isPasted">Edge AI holds immense significance in the current and future technology landscape. With decentralized AI processing, improved responsiveness, enhanced privacy and security, cost-efficiency, scalability, and distributed computing, Edge AI is revolutionizing our world as we speak. And with the rapid developments happening constantly, it may be difficult to follow all the new advancements in the field.&nbsp;</p><p dir="ltr" id="isPasted">That is why Wevolver has collaborated with several industry experts, researchers, professors, and leading companies to create a comprehensive report on the current state of Edge AI, exploring its history, cutting-edge applications, and <a href="https://www.wevolver.com/article/2023-edge-ai-technology-report-chapter-x-future-of-edge-ai">future developments</a>. This report will provide you with practical and technical knowledge to help you understand and navigate the evolving landscape of Edge AI.</p><p dir="ltr">This report would not have been possible without the esteemed contributions and sponsorship of Alif Semiconductor, Arduino, Arm, Axelera AI, BrainChip, Edge Impulse, GreenWaves Technologies, Sparkfun, ST, and Synaptics. Their commitment to objectively sharing knowledge and insights to help inspire innovation and technological evolution aligns perfectly with what Wevolver does and the impact it aims to achieve.</p><p dir="ltr">As the world becomes increasingly connected and data-driven, Edge AI is emerging as a vital technology at the core of this transformation, and we hope this comprehensive report provides all the knowledge and inspiration you need to participate in this technological journey.</p><p dir="ltr"><strong>Samir Jaber</strong><br>Editor-in-Chief</p><p dir="ltr"><em>This text was contributed to by engineers from&nbsp;</em><span style="background-color: transparent;"><em>Arm,&nbsp;</em></span><span style="background-color: transparent;"><em>Axelera AI, and&nbsp;</em></span><span style="background-color: transparent;"><em>BrainChip.</em></span></p><p dir="ltr"><span style="background-color: transparent;"><em><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjg3ODg2MDk4NTA0LUVkZ2UgQUkgRG93bmxvYWQgYmFubmVyICgxKS5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 302px;" class="fr-fic fr-dib"></em></span></p><p><em><span class="fr-video fr-deletable fr-fvc fr-dvb fr-draggable" contenteditable="false" draggable="true"><iframe width="540" height="335" src="https://c8056756.sibforms.com/serve/MUIFABFML2jk7knWjihwfmaWcFVV6GvM3WWUP6qryWvjht4DWOrppyQ7-VLwYu9wbjkQCCMxaWS9vmneqio8zYAai1OKHh0-OA61OuFt5xdYgRxECqvgNTAnjjob8456fOSNTOOCe8YLBqs4fiLh0kTJSsBgWCDDvnFUMZSuafNiO9Cbhh5H7fBa4doE8a4CzpRXWMOtAOLUtRmq" 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><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span></iframe></span></em></p><hr><h2><span style="background-color: transparent;">The 2023 Edge AI Technology Report</span></h2><p dir="ltr"><em>The guide to understanding the state of the art in hardware &amp; software in Edge AI.</em></p><p dir="ltr">Click through to read each of the report's chapters.&nbsp;</p><h3><a href="https://www.wevolver.com/article/2023-edge-ai-technology-report-chapter-i-overview-of-industries-application-use-cases/draft" rel="noopener noreferrer" target="_blank">Chapter I: Overview of Industries and Application Use Cases</a></h3><h3 dir="ltr"><a href="https://www.wevolver.com/article/2023-edge-ai-technology-report-chapter-ii-advantages-of-edge-ai" rel="noopener noreferrer" target="_blank">Chapter II: Advantages of Edge AI</a></h3><h3><a href="https://www.wevolver.com/article/2023-edge-ai-technology-report-chapter-iii-edge-ai-platforms" rel="noopener noreferrer" target="_blank">Chapter III: Edge AI Platforms</a></h3><h3><a href="https://www.wevolver.com/article/2023-edge-ai-technology-report-chapter-iv-hardware-software-selection" rel="noopener noreferrer" target="_blank">Chapter IV: Hardware and Software Selection</a></h3><h3><a href="https://www.wevolver.com/article/2023-edge-ai-technology-report-chapter-v-tinyml/draft" rel="noopener noreferrer" target="_blank">Chapter V: Tiny ML</a></h3><h3><a href="https://www.wevolver.com/article/2023-edge-ai-technology-report-chapter-vi-edge-ai-algorithms" rel="noopener noreferrer" target="_blank">Chapter VI: Edge AI Algorithms</a></h3><h3><a href="https://www.wevolver.com/article/2023-edge-ai-technology-report-chapter-vii-sensing-modalities" rel="noopener noreferrer" target="_blank">Chapter VII: Sensing Modalities</a></h3><h3><a href="https://www.wevolver.com/article/2023-edge-ai-technology-report-chapter-viii-case-studies" rel="noopener noreferrer" target="_blank">Chapter VIII: Case Studies</a></h3><h3 dir="ltr"><a href="https://www.wevolver.com/article/2023-edge-ai-technology-report-chapter-ix-challenges-of-edge-ai" rel="noopener noreferrer" target="_blank">Chapter IX: Challenges of Edge AI</a></h3><h3><a href="https://www.wevolver.com/article/2023-edge-ai-technology-report-chapter-x-future-of-edge-ai" rel="noopener noreferrer" target="_blank">Chapter X: The Future of Edge AI and Conclusion</a></h3><hr><h2 id="isPasted"><strong>How this report came to be: A collaborative effort</strong></h2><p>This report is the culmination of collaborative efforts among a diverse team of contributors. Initial input was graciously provided by our sponsors and partner, who shared their expert opinions and insightful case studies, creating a strong technical foundation. A dedicated team of writers further expanded these texts under the careful guidance of our Editor-in-Chief. Thank you to our report sponsors and report partner, the tinyML Foundation. &nbsp;</p><p><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjg3ODgxMTAwNjI0LTExICgxKS5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib"></p><h3>About the writers</h3><h3 dir="ltr">Samir Jaber, Editor-in-chief.&nbsp;</h3><p dir="ltr"><a href="https://www.wevolver.com/profile/samir.jaber" rel="noopener noreferrer" target="_blank">Samir Jaber</a> is an SEO &amp; Content Specialist with a background in engineering, nanotechnology, and scientific research. Samir has comprehensive experience working with major engineering and technology companies as a writer, editor, and digital marketing consultant. He is an expert in content management and strategy, particularly in the engineering and tech fields. He is a featured author in 30+ industrial magazines with a focus on IoT, nanotechnology, materials science, engineering, and sustainability. Samir is also an award-winning engineering researcher in the fields of nanofabrication and microfluidics.</p><h3 dir="ltr">Dr. John Soldatos</h3><p dir="ltr"><a href="https://www.wevolver.com/profile/john.soldatos" rel="noopener noreferrer" target="_blank">John Soldatos</a> 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><h3 dir="ltr">Dr. Miroslav Milovanovic</h3><p dir="ltr"><a href="https://www.wevolver.com/profile/miroslav.milovanovic" rel="noopener noreferrer" target="_blank">Miroslav Milovanovic</a> has a Ph.D. in Computer Science and Electrotechnics and works as an assistant professor at the Faculty of Electronic Engineering. He teaches courses strongly connected with Data Science, the Industrial Internet of Things, Modern Control of Industrial Processes, and Intelligent control.&nbsp;</p><p dir="ltr">Additionally, Dr. Milovanovic is the Chief of the Laboratory for Intelligent Control in the Control Systems Department. His instructional expertise encompasses courses strongly connected with Data Science, the Industrial Internet of Things, Deep Learning, Machine Learning in Python, Modern Control of Industrial Processes, and Intelligent Control. By imparting practical knowledge in these areas, he empowers students to excel in their chosen fields. With over 45 published scientific papers, his research primarily revolves around Data Science, Deep Learning, and their practical applications.</p><h3 dir="ltr">Lydia Husser</h3><p dir="ltr"><a href="https://www.wevolver.com/profile/lydia.husser" rel="noopener noreferrer" target="_blank">Lydia Husser</a> is a technical writer in robotics and a freelance writer captivated by innovative science and engineering. She holds a degree in Computer Science and credentials in electronics technology. Her background includes work in robotics, communications hardware, and developer documentation.&nbsp;</p><p dir="ltr">The cover image of the report was created using generative AI tools. Credit to Muthali Ganesh.</p><p dir="ltr"><br></p>

The guide to understanding the state of the art in hardware & software in Edge AI.

2023 Edge AI Technology Report

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<h2>Introduction</h2><p id="isPasted">The need for an efficient AI inference system is required to continue the explosion of AI applications. Renesas&rsquo; RZ/V2M is one of the first RZ/V2 series products to address these issues. Based on the reconfigurable processor technology Renesas has developed over many years, they have created the DRP-AI (Dynamically Reconfigurable Processor for AI), an AI accelerator with high-speed AI inference processing that delivers the low power and flexibility demanded by endpoints. This article provides a case study of this product, exploring the technology and its impact on the user.</p><h2>AI Applications</h2><p>To set the context, we will first explore the two main components involved in creating an AI application; Training and inference.</p><p><span class="fr-img-caption fr-fic fr-dib" style="width: 302px;"><span class="fr-img-wrap"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjgzNjMwODIwOTMxLWltYWdlMS5qcGVnIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 300px;" class="fr-fic fr-dib"><span class="fr-inner">Figure 1: Training involves learning the optimized weights from the dataset, and inference is passing the unseen data to the network with the optimized weights and getting the prediction. Source: Image by Author</span></span></span></p><style type="text/css" id="isPasted">
p.p1 {margin: 0.0px 0.0px 6.0px 0.0px; font: 16.0px Arial; color: #000000} p.p2 {margin: 0.0px 0.0px 18.0px 0.0px; font: 11.0px Arial; color: #000000} p.p3 {margin: 0.0px 0.0px 16.0px 0.0px; font: 11.0px Arial; color: #000000} p.p4 {margin: 0.0px 0.0px 4.0px 0.0px; font: 14.0px Arial; color: #333333} p.p5 {margin: 0.0px 0.0px 0.0px 0.0px; font: 11.0px Arial; color: #000000; min-height: 12.0px} p.p6 {margin: 0.0px 0.0px 0.0px 0.0px; font: 11.0px Arial; color: #000000} p.p7 {margin: 0.0px 0.0px 20.0px 0.0px; font: 11.0px Arial; color: #000000} p.p8 {margin: 0.0px 0.0px 6.0px 0.0px; font: 20.0px Arial; color: #000000} span.s1 {font: 7.3px Arial}
</style><h3>Training</h3><p>Model training is a phase in the artificial intelligence development lifecycle where practitioners try to fit the best combination of weights and bias to a machine learning algorithm to minimise a loss function over the prediction range. Model training aims to build the best mathematical representation of the relationship between data features and a target label (in supervised learning) or among the features themselves (in unsupervised learning).<sup>[1]</sup></p><h3>Inference</h3><p>This model can be deployed after the training to predict unseen data. This is called inference. We must be able to apply this in real-time endpoint devices to scale this solution.</p><h2>Challenges in AI inference</h2><p>As the user experience gets more advanced, End devices require AI models that can process inferences based on deep learning to take the place of human senses like hearing and vision. To implement AI in endpoints, the main concerns to be addressed are power consumption, size and flexibility. While cloud servers can have high computational powers equipped with high power and cooling devices, the endpoint device needs to work utilising low power consumption with minimum size. This can result in heat generation, inconsistent inference speed due to heating, shorter run times and increased cost.&nbsp;</p><p>So when it comes to power consumption saving, the best way to achieve that is to customise and optimise the hardware requirements to the specific AI processing. However, AI is a field evolving daily with significant impact, and thus, the hardware will become obsolete soon. Hence, the endpoint devices should be flexible enough to support newly developed AI models.&nbsp;</p><p>The limited cooling ability results in overheating of the processing unit, and when the unit starts emitting heat above a threshold, the memory and core frequencies of the unit are reduced to lower the heat emission. This is called `Thermal throttling`<sup>[2]</sup> and results in inconsistent inference speeds.&nbsp;</p><p>&nbsp;<span style="background-color: transparent;">Immense research has been going on to make edge devices more computationally capable while utilising low power that allows real-time offline implementations of artificial intelligence solutions. According to Statista, the AI semiconductor market is expected to grow from $32 billion in 2020 to $65 billion in 2025 worldwide. It&rsquo;s important to note that the market was only $17 billion in 2017</span><sup style="background-color: transparent;">[3]</sup><span style="background-color: transparent;">.</span></p><p>Renesas, one of the leading contributors in the field of embedded systems, has created a new product, Renesas RZ/V2M, one of the first in the RZ/V series, that delivers low-power, low-heat, stable and flexible AI inference systems for edge devices. &nbsp;</p><h2>Renesas RZ/V2M</h2><p>Renesas RZ/V2M is a Microprocessing unit (MPU) optimised for image-based AI inference (Vision AI) to deliver lower power consumption and stable AI inference. The key technology that makes this product is the DRP-AI technology. While most AI accelerators specialise only in AI inference and rely on the CPU for pre- and post-processing, DRP-AI integrates pre- and post-processing and AI inference into a single DRP-AI hardware to achieve superior AI processing performance<sup>[4]</sup>.</p><p><span class="fr-img-caption fr-fic fr-dib" style="width: 302px;"><span class="fr-img-wrap"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjgzNjMwODQwODk1LWltYWdlMi0yLnBuZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 300px;" class="fr-fic fr-dib"><span class="fr-inner">Figure 2: Features of RZ/V2M. Source: [5]</span></span></span></p><p><span style="background-color: transparent;">RZ/V2M is being adopted in applications such as surveillance cameras, industrial cameras, marketing cameras, gateways, robots, and medical equipment using AI inference, and these products will be shipped to the market soon</span><sup style="background-color: transparent;">[5]</sup><span style="background-color: transparent;">.</span></p><h2>The brain of the product, DRP-AI</h2><p>The core technology behind this MPU is the DRP-AI technology. The DRP-AI is hardware dedicated to AI inference with flexibility, high-speed processing, and power efficiency.&nbsp;</p><p>As the name suggests, it contains two components; the DRP (reconfigurable processor) and AI-MAC (multiply-accumulate processor). The Deep Learning network operations are split into these components. The convolution and fully connected layers are assigned to the AI-MAC, and all other operations, such as pre-processing and pooling, are assigned to the DRP.&nbsp;</p><p><span class="fr-img-caption fr-fic fr-dib" style="width: 302px;"><span class="fr-img-wrap"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjgzNjMwODU1ODQzLWltYWdlMy0yLnBuZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 300px;" class="fr-fic fr-dib"><span class="fr-inner">Figure 3: Internal working of AI inference using DRP-AI. Source: [6]</span></span></span></p><style type="text/css" id="isPasted">
p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 11.0px Arial; color: #000000} span.s1 {font: 7.3px Arial}
</style><p>The DRP-AI translator is the software provided to enable users to easily implement AI models optimized to maximize the performance of DRP-AI on this flexible hardware. Multiple executables output by the DRP-AI translator can be placed in external memory. This makes it possible to dynamically switch between multiple AI models. In addition, the DRP-AI translator can be continuously updated to support newly developed AI models without hardware changes<sup>[4]</sup>. &nbsp;The DRP-AI Translator converts the originally trained weights to DRP-AI optimized executables independent of various AI frameworks.&nbsp;</p><p><span class="fr-img-caption fr-fic fr-dib" style="width: 302px;"><span class="fr-img-wrap"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjgzNjMwODczMzc5LWltYWdlNC0yLnBuZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 300px;" class="fr-fic fr-dib"><span class="fr-inner">Figure 4: Training a model on the server and using a DRP-AI translator to DRP-AI to perform inference. Source: [7]</span></span></span></p><style type="text/css" id="isPasted">
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</style><h2>Impact</h2><p>To measure the impact of the AI processor, a popular object classifier, TinyYolov2<sup>[8]</sup> was implemented in the DRP-AI test device and a commercially available GPU server regulated under the same conditions. The surface temperature of both systems was measured using thermography. The DRP-AI test device had a surface temperature of 40.9 degrees without using any heat sink when the TinyYolov2 was running at 42 fps. The GPU server had a surface temperature of 79 degrees, even with a heat sink installed running at a lower frame rate than DRP-AI.&nbsp;</p><p><span class="fr-img-caption fr-fic fr-dib" style="width: 302px;"><span class="fr-img-wrap"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjgzNjMwODk5NzU1LWltYWdlNi5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib"><span class="fr-inner">Figure 5: The surface temperature during inference. Source: [4]</span></span></span></p><style type="text/css" id="isPasted">
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</style><p>The integrated RZ/V2M was further compared with a standard AI accelerator to evaluate the overall power consumption and efficiency by running YOLOv3-tiny<sup>[9]</sup> under regulated conditions. In addition, the performance was evaluated for another standard object classification model, MobileNet v2<sup>[10]</sup>. The results are listed in the below table.</p><p id="isPasted"><em>Table 1. Performance comparison of RZ/V2M and other Accelerators, &nbsp;source:[5]</em></p><table cellpadding="0" cellspacing="0"><tbody><tr><td valign="bottom"><p><br></p></td><td valign="bottom"><p>RZ/V2M</p><p>MobileNet V2</p></td><td valign="bottom"><p>RZ/V2M</p><p>YOLOv3-tiny</p></td><td valign="bottom"><p>Other AI accelerator</p><p>YOLOv3-tiny (Reference)</p></td></tr><tr><td valign="bottom"><p>AI performance (fps)</p></td><td valign="bottom"><p>55fps</p></td><td valign="bottom"><p>52fps</p></td><td valign="bottom"><p>46fps</p></td></tr><tr><td valign="bottom"><p>Power consumption</p></td><td valign="bottom"><p>2.6W</p></td><td valign="bottom"><p>3.0W</p></td><td valign="bottom"><p>9.8W</p></td></tr><tr><td valign="bottom"><p>Power efficiency</p></td><td valign="bottom"><p>21.3fps/W</p></td><td valign="bottom"><p>17.2fps/W</p></td><td valign="bottom"><p>4.7fps/W</p></td></tr></tbody></table><p>As the table indicates, the RZ/V2M MPU can process at a higher frame rate with just ~3.0W power usage with a ~17fps/W which is 3.5 times more efficient than a regular AI accelerator. &nbsp;The surface temperature of the RZ/V2M was 29.7 degrees without any heat sinks compared to the 75 degrees with heat sink by the regular AI accelerator.&nbsp;</p><h2>Conclusion</h2><p>Overall, RZ/V2M is an MPU designed to implement deep learning models, specifically Vision AI, in edge devices with low power, stable performance, low heat emission, and flexible user modifications. The DRP-AI is the core technology that allows this high performance. This product is a stepping stone to developing advanced edge devices that can react intelligently in real-time. Visit Renesas&rsquo; website to read more about these products<sup>[11]</sup>.</p><h2>References</h2><p><br></p><p>[1] Siebel, T. M. (n.d.). Model Training. C3 AI. Retrieved October 24, 2022, from <a href="https://c3.ai/glossary/data-science/model-training/" target="_blank">https://c3.ai/glossary/data-science/model-training/</a></p><p>[2] <em>Everything You Need To Know About Thermal Throttling</em>. (2021, September 22). Electronics Hub. Retrieved October 27, 2022, from <a href="https://www.electronicshub.org/thermal-throttling/" target="_blank">https://www.electronicshub.org/thermal-throttling/</a></p><p>[3] Alsop, T. (2022, April 19). <em>Global AI semiconductor market size 2025</em>. Statista. Retrieved October 24, 2022, from <a href="https://www.statista.com/statistics/1100690/ai-semiconductor-market-size-worldwide/" target="_blank">https://www.statista.com/statistics/1100690/ai-semiconductor-market-size-worldwide/</a></p><p>[4] Cognitive Product Department, IoT and Infrastructure Business Unit, Renesas Electronics Corporation. (2021, June). Embedded AI-Accelerator DRP-AI. Retrieved October 27, 2022, from <a href="https://www.renesas.com/kr/en/document/whp/embedded-ai-accelerator-drp-ai?r=1526446" target="_blank">https://www.renesas.com/kr/en/document/whp/embedded-ai-accelerator-drp-ai?r=1526446</a></p><p>[5] Yamano, S. (n.d.). Farewell, Heat Countermeasure: RZ/V2M Brings the Innovation for AI Products. Renesas. Retrieved October 27, 2022, from <a href="https://www.renesas.com/us/en/blogs/farewell-heat-countermeasure-rzv2m-brings-innovation-ai-products" target="_blank">https://www.renesas.com/us/en/blogs/farewell-heat-countermeasure-rzv2m-brings-innovation-ai-products</a></p><p>[6] Renesas Electronics Corporation. (n.d.). <em>AI Accelerator: DRP-AI</em>. Renesas. Retrieved October 27, 2022, from <a href="https://www.renesas.com/us/en/application/key-technology/artificial-intelligence/ai-accelerator-drp-ai%23overview" target="_blank">https://www.renesas.com/us/en/application/key-technology/artificial-intelligence/ai-accelerator-drp-ai#overview</a></p><p>[7] Renesas Electronics Corporation. (2022, January 17). Renesas. Retrieved October 27, 2022, from <a href="https://www.renesas.com/kr/en/application/key-technology/artificial-intelligence/ai-accelerator-drp-ai/ai-tool-drp-ai-translator%23overview" target="_blank">https://www.renesas.com/kr/en/application/key-technology/artificial-intelligence/ai-accelerator-drp-ai/ai-tool-drp-ai-translator#overview</a></p><p>[8] YOLO: Real-Time Object Detection. (n.d.). Joseph Redmon. Retrieved October 27, 2022, from <a href="https://pjreddie.com/darknet/yolov2/" target="_blank">https://pjreddie.com/darknet/yolov2/</a></p><p>[9] YOLO: Real-Time Object Detection. (n.d.). Joseph Redmon. Retrieved October 27, 2022, from <a href="https://pjreddie.com/darknet/yolo/" target="_blank">https://pjreddie.com/darknet/yolo/</a></p><p>[10] Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., &amp; Chen, L. (2018). MobileNetV2: Inverted Residuals and Linear Bottlenecks. arXiv. <a href="https://doi.org/10.48550/arXiv.1801.04381" target="_blank">https://doi.org/10.48550/arXiv.1801.04381</a></p><p>[11] Renesas Electronics Corporation. Retrieved October 27, 2022, from <a href="https://www.renesas.com/us/en" target="_blank">https://www.renesas.com/us/en</a></p><style type="text/css" id="isPasted">
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