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<table style="width: 100%;"><tbody><tr><td style="width: 100%;">Catch up on our recent webinar: Powering Edge and Endpoint Applications: A deep dive into Reality AI Tools® from RenesasMohammed Dogar, Vice President, Head of Global Business Development &amp; Ecosystem, and Jeff Sieracki, Director of AI COE, will sit with tech writer Samir Jaber to unpack the Reality AI Tools suite and discuss their impact on Edge AI applications. Why Watch?<ul><li dir="ltr">Learn: Understand the primary functions and benefits of using Reality AI Tools®</li><li dir="ltr">Connect: Hear from Mohammed Dogar, Vice President, Head of Global Business Development &amp; Ecosystem, and Jeff Sieracki, Director of AI COE</li><li dir="ltr">Engage: Opportunities to ask questions and interact with our experts</li></ul>Check it out here:<iframe width="640" height="360" src="https://www.youtube.com/embed/kjISoxQmpU0?&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">&nbsp;</iframe></td></tr></tbody></table>We are 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.Report Highlights:<ul><li dir="ltr">#1:&nbsp;Industry-specific insights–&nbsp;Each industry is covered by a dedicated chapter that provides industry-specific insights, descriptions, and examples. ,</li><li dir="ltr">#2: Real-life case studies – Get comprehensive insights from featured sections of real-world case studies.&nbsp;</li><li 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.</li></ul>Download the full report below.&nbsp;<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"><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">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</iframe><h2 dir="ltr">Report Introduction</h2>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.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.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?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.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.Samir Jaber, Report Editor-in-Chief<h2 dir="ltr">About the report</h2>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.<h3 dir="ltr">About the authors</h3>Samir JaberEditor-in-Chief Leipzig, Germany&nbsp;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.John SoldatosCo-author Athens, Greece&nbsp;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;Ravi RaoCo-author Ahmedabad, India&nbsp;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.<h2>About the Sponsors</h2>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.<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"><h2>Download your copy of the 2024 State of Edge AI Report now.&nbsp;</h2><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">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</iframe>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;

Introduction

We are pleased to share Chapter 1 of the&nbsp;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.Report Highlights:<ul><li dir="ltr">#1:&nbsp;Industry-specific insights–&nbsp;Each industry is covered by a dedicated chapter that provides industry-specific insights, descriptions, and examples. ,</li><li dir="ltr">#2: Real-life case studies – Get comprehensive insights from featured sections of real-world case studies.&nbsp;</li><li 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.</li></ul>Download the full report below.&nbsp;<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"><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">&nbsp;&nbsp;</iframe><h2 dir="ltr">The Evolution of AI: From Research to Edge Technology</h2>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.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.<blockquote>“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</blockquote>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.<h3 dir="ltr" id="isPasted">Edge AI Market Landscape</h3>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.<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">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)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.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.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.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.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.<h3 dir="ltr" id="isPasted">Industry Adoption and Trends of Edge AI</h3>“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;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).<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">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)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.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;<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">How organizations have been using AI in the past two years (Credit: IBM)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.<hr><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"><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>

Edge AI Market Analysis and Trends

We are thrilled to share Chapter 2 of the&nbsp;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.Report Highlights:<ul><li dir="ltr">#1:&nbsp;Industry-specific insights–&nbsp;Each industry is covered by a dedicated chapter that provides industry-specific insights, descriptions, and examples. ,</li><li dir="ltr">#2: Real-life case studies – Get comprehensive insights from featured sections of real-world case studies.&nbsp;</li><li 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.</li></ul>Download the full report below.&nbsp;<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"><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> <h2 dir="ltr">Healthcare and Medical Applications</h2>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.<h3 dir="ltr">Real-Time Patient Monitoring</h3>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.<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">Edge AI is transforming the medical industry by enabling digital diagnosis and remote patient monitoring (Credit: Jenny Wang, Seeed Studio)<table style="width: 100%;"><tbody><tr><td style="width: 100%;">CASE STUDY: IMAGIMBOB<h2 id="isPasted">A Fast, Cost-Effective, and Reliable Way to Add Smart Features to Healthcare Devices</h2>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.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.<h2>The Fastest Way to Launch Smart AI Features</h2>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;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;<h3>Developing Sharper Models in Real Time&nbsp;</h3>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.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;“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.”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>.</td></tr></tbody></table><h2 dir="ltr">On-Device Medical Imaging and Smart Rehabilitation</h2>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.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.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.<h3 dir="ltr">Clinical Trials with Real-World Data</h3>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;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.<h3 dir="ltr">Prediction, Detection, and Tracking of Disease Outbreaks</h3>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.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;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.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.<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">Artificial intelligence-enabled remote patient monitoring architectures (Credit: Shaik, T. et al., WIREs) 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.<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"><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>

Healthcare and Medical Applications

Industrial IoT and Manufacturing

Smart Cities and Urban Infrastructure

We are pleased to share chapter 5 of 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.Report Highlights:<ul><li dir="ltr">#1:&nbsp;Industry-specific insights–&nbsp;Each industry is covered by a dedicated chapter that provides industry-specific insights, descriptions, and examples. ,</li><li dir="ltr">#2: Real-life case studies – Get comprehensive insights from featured sections of real-world case studies.&nbsp;</li><li 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.</li></ul>Download the full report below.&nbsp;<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"><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> <h1 id="isPasted">Retail and Customer Experience</h1>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.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.<blockquote>“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</blockquote><h3 dir="ltr" id="isPasted">Inventory Management Perfected</h3>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;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.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.<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">Shelves with digital displays for price tags (Credit: MediaTile) 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;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.<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>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.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;The e-commerce boom brought FrioCONNECT challenges: inventory was inaccurate, counts were tedious, and 2% of listings were oversold or incorrectly shipped.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.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.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; <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">Sony’s vision-based AI for inventory management and automation (Credit: Sony)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.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.</td></tr></tbody></table><h3 dir="ltr" id="isPasted">Customer Behavior Analysis: Personalization at Scale</h3>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;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.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.<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">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). 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.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.<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>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.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.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;<ul><li dir="ltr">Generating insights for retailers’ decision-making</li><li 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</li><li dir="ltr">Automating manual tasks to augment the workforce and minimize error rates</li><li dir="ltr">Enhancing overall customer experience as consumers increasingly return to in-person shopping</li></ul>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>.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>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.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.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.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.<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">In addition to security and surveillance, cameras can play a crucial role in gathering valuable insights into customer behavior, enabling personalized shopping experiences.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.<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"><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>

Retail and Customer Experience

We are pleased to share chapter 6 of 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.Report Highlights:<ul><li dir="ltr">#1:&nbsp;Industry-specific insights–&nbsp;Each industry is covered by a dedicated chapter that provides industry-specific insights, descriptions, and examples. ,</li><li dir="ltr">#2: Real-life case studies – Get comprehensive insights from featured sections of real-world case studies.&nbsp;</li><li 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.</li></ul>Download the full report below.&nbsp;<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"><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> 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.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.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.<blockquote>“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</blockquote><h2>Smart Energy Monitoring for Consumer Awareness and Cost Savings</h2>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;<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">Edge-AI-powered smart energy meters revolutionize energy management by providing precise, real-time data on consumption.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.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.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.<h2>Innovations in Renewable Energy Integration Powered by Edge AI</h2>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;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.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.<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">Integrating renewable energy poses challenges due to its variability and the need for advanced grid management to ensure reliability and stability.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.<h2>Proactive Smart Grid Management through Edge AI Solutions</h2>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.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.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;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.<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">Optimizing EV charging infrastructure with Edge AI is crucial for balancing grid demands and facilitating the transition to electric mobility (Credit: Ather Energy)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.<h2>Transforming the Conventional Energy Sector with Edge AI</h2>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;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.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.<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"><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>

Energy Efficiency and Sustainability

We are pleased to share chapter 7 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.Report Highlights:<ul><li dir="ltr">#1:&nbsp;Industry-specific insights–&nbsp;Each industry is covered by a dedicated chapter that provides industry-specific insights, descriptions, and examples. ,</li><li dir="ltr">#2: Real-life case studies – Get comprehensive insights from featured sections of real-world case studies.&nbsp;</li><li 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.</li></ul>Download the full report below.&nbsp;<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"><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> <h2 dir="ltr">Agriculture and Food Production</h2>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;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.<blockquote>“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</blockquote><h3 dir="ltr" id="isPasted">Improved Crop Monitoring and Analytics for Maximizing Yield</h3>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.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.<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">. 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).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.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.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.<h3 dir="ltr">Streamlining Livestock Management with Edge AI</h3>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.<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">Edge AI and IoT can come together and enhance herd management, enabling real-time tracking of health and behavior. (Credit: Nvidia)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.<h3 dir="ltr">Food Quality Assurance</h3>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;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.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;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.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.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.<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"><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>

Agriculture and Food Production 

<h3 dir="ltr" id="isPasted">An Edge-AI-Powered Automotive Sector</h3>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.<blockquote>“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</blockquote><h3 dir="ltr" id="isPasted">Autonomous Vehicles: Enhancing Safety and Efficiency on the Road</h3>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;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;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.<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">The Holon Mover, an autonomous mini-bus (Credit: Benteler)<h3 dir="ltr" id="isPasted">Real-Time Traffic Management and Smart Parking</h3>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.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.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.<h3 dir="ltr" id="isPasted">Electric Vehicles: Enhancing Battery Management and Charging Infrastructure Optimization</h3>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;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.<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">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).<h3 dir="ltr" id="isPasted">Fleet Management and Traffic Sign Recognition</h3>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.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.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.

Automotive and Transportation

<h3 dir="ltr" id="isPasted">Where Gen AI Meets Edge Computing</h3>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;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.<blockquote>“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</blockquote><h3 dir="ltr" id="isPasted">LLMs at the Edge: The Challenges</h3>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;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.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.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;<h3 dir="ltr">Enabling LLMs and Edge Computing Convergence</h3>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.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.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.<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">BrainChip’s AKD1000 AIoT chip brings AI and edge together, leveraging its Akida™ advanced neural networking processor (Credit: BrainChip)<h3 dir="ltr" id="isPasted">Examples of Real-Life Edge LLM Systems</h3>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.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.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.<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; 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; 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; 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. 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>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:<ul><li 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.</li><li 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.</li><li 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.</li><li 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.</li><li 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.</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; 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; 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; 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; 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>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.

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