In the modern era of sport, results are often decided by ultra-fine margins like milliseconds or millimeters. Yet even precise metrics such as speed and distance are still quite rudimentary in nature. The real wins for athletes, coaches, and sporting teams are gained through a much deeper understanding of sports performance and potential improvement. To achieve these goals and help obtain a competitive advantage, the latest performance tracking solutions lean on the power of wireless connectivity and edge processing. <h2>Monitoring unique performance metrics in sport </h2>Combining dozens of datapoints makes it possible to build a unique profile of an individual’s abilities. Examples in each sport might include the athlete’s body movement during key actions, or the position, launch angle, spin rate, and acceleration rate of the bat or ball at various game stages. For individual sports like athletics and cycling, key metrics might focus on biomechanical efficiency and technical precision. For team sports including the various football codes, common metrics include sprints, accelerations, decelerations, and workload intensity.  <h2>Bluetooth LE turns raw data into powerful insights </h2>Sports tracking solutions based on wireless tech make these and other powerful metrics readily available, in turn helping sports industry stakeholders achieve more nuanced insights. Tools like inertial sensors, anchor devices, and location services form systems that measure sports parameters in near real time. The high volume of raw data generated can be processed and analyzed using algorithms running on the sensor or wearable’s integrated wireless device before the information is forwarded—using low latency wireless connectivity such as <a href="https://www.nordicsemi.com/Products/Wireless/Bluetooth-Low-Energy">Bluetooth LE</a>—to an accompanying app on the user’s smartphone or a web-based dashboard on their laptop. <h2>Ball and athlete tracking technology in action </h2>Ball tracking technology, for example, delivers data on the trajectory, spin, and bounce of the ball, helping to measure the quality and accuracy of passes and shots, or evaluate defensive and offensive positioning. Athlete tracking technology, meanwhile, provides insights on the precise position, movement, and activity of the players themselves to help measure their performance, fitness, workload, and tactical decision-making. Moreover, wearable tech attached to the wrist, chest, or other parts of the body enables a much deeper dive into data, potentially revealing key information about an athlete’s heart rate variability (HRV), VO2 max, muscle fatigue, lactic acid, power output, and other important physical parameters. Such insights can provide a better understanding of athletic training, recovery, and injury prevention. <h2>A Nordic Bluetooth LE soccer tracking solution </h2>One prominent example of wireless sports technology in action is the Nordic-powered Apex Series, used by a host of major international sporting organizations including English Premier League football clubs.  Embedded in a custom lightweight vest worn by the athlete, STATSports’ <a href="https://www.nordicsemi.com/Nordic-news/2018/11/STATSports-APEX-device-employs-nRF52840-to-wirelessly-sync-comprehensive-sports-specific-metrics">Apex</a> employs Bluetooth LE connectivity to wirelessly sync sports-specific metrics to smartphones and tablets for coaches to make informed decisions during a match or training session. The device integrates a range of sensors including a high impact accelerometer, gyroscope, magnetometer, and augmented GNSS. These sensors provide comprehensive data including heart rate, total distance covered, current and maximum speed, number of sprints, accelerations, impacts, dynamic stress load, and step balance.  <h2>Edge AI for predictive modeling </h2>But the ultimate game-changer in the sports sector comes in the form of predictive modeling and data-driven analytics performed at the edge. When relevant data is not only crunched but analyzed and interpreted using the power of ‘<a href="https://www.nordicsemi.com/Products/Technologies/Edge-AI">Edge AI</a>’ algorithms, sports analysts can take advantage of even more precise and personalized insights. Adding AI and Machine Learning (ML) to the mix allows, for example, a sports wearable with the same sensors as before to now continuously monitor several performance parameters simultaneously, uncovering meaningful patterns that were previously hidden. By analyzing both historical data and real-time sensor information, statistical models can pinpoint factors that have a major impact on the way athletes prepare and perform, not to mention identify opposition strategies and gauge overall team dynamics. <h2>nRF54 Series SoCs enable the future of sports analytics devices </h2>Behind every breakthrough in next-generation sports tracking is hardware built to handle extreme demands. The fourth-generation wireless SoCs from Nordic Semiconductor, the nRF54 Series, deliver exactly that – combining unmatched processing power, ultra-low power consumption, and advanced on-chip AI capabilities.  To make sense of all the available sensor data in context and quickly enough to enable rapid and effective decision-making, powerful integrated wireless SoCs such as the nRF54L Series provide the serious processing heft to perform the ML and sensor fusion that forms part of Edge AI. By running Edge AI using a Nordic Semiconductor nRF54 Series SoC, for example, the local processing will use less power than sending data over the air, allowing a sports analytics device to have longer operation and smaller battery size.  The result is sophisticated sports analytics solutions that are changing the game.

In the modern era of sport, results are often decided by ultra-fine margins like milliseconds or millimeters. Yet even precise metrics such as speed and distance are still quite rudimentary in nature.

How next-gen wearables and Edge AI improve sports performance analytics

<h3 id="isPasted">The Importance of Edge AI Deployments</h3>Edge AI refers to the practice of running artificial intelligence models directly on edge devices, such as sensors, smartphones, or industrial machines, rather than relying solely on centralized cloud servers. Some Arduino boards based on a microcontroller – such as those in the Portenta family (<a href="https://www.arduino.cc/pro/hardware-product-portenta-h7" target="_blank">Portenta H7</a>, <a href="https://www.arduino.cc/pro/hardware-product-portenta-c33" target="_blank">Portenta C33</a>) or <a href="https://www.arduino.cc/pro/hardware-arduino-opta/" target="_blank">Opta</a> – allow running Edge AI natively, leveraging tools like Tiny Machine learning. This approach offers several advantages:<ul><li>Reduced Latency: Processing data locally enables real-time decision-making, which is essential for applications like autonomous vehicles and industrial automation.</li><li>Enhanced Privacy: Keeping data on-device minimizes the risk of exposure, addressing privacy concerns in sectors like healthcare and finance.</li><li>Bandwidth Efficiency: Local processing decreases the need to transmit large volumes of data to the cloud, reducing bandwidth usage and associated costs.</li></ul>However, deploying AI models on edge devices presents challenges, including limited computational resources and the need for efficient model management. <h3>Leveraging Docker for Edge AI </h3>Docker containers encapsulate applications and their dependencies into isolated environments, ensuring consistency across deployments. This is particularly beneficial for edge AI due to:<ul><li>Simplified Deployment: Containers bundle the application code, libraries, and configurations, eliminating dependency conflicts and streamlining the deployment process.</li><li>Portability: Docker ensures that applications run uniformly across different environments, making it easier to deploy models on various edge devices.</li><li>Resource Efficiency: Containers are lightweight compared to traditional virtual machines, making them suitable for devices with constrained resources.</li></ul>Implementing Docker for edge AI deployments can lead to more manageable and scalable solutions, as it abstracts the underlying hardware and provides a uniform interface for application deployment.<h3>Implementing Edge Impulse Models with Docker </h3>To deploy an Edge Impulse model using Docker on an embedded Linux platform, follow these steps:<ol><li>Model Training: Develop and optimize your machine learning model using the Edge Impulse platform, which provides tools for collecting data, designing algorithms, and validating performance.</li><li>Container Setup: Utilize a pre-configured Docker image that includes the necessary runtime libraries for your model. This involves creating a Dockerfile that specifies the base image, installs dependencies, and copies the model into the container.</li><li>Deployment on Embedded Linux: Execute the containerized model on your target hardware, leveraging its processing capabilities for efficient inference. Docker’s portability ensures that the model runs consistently across different devices.</li></ol>This methodology simplifies AI implementation, making it accessible for engineers involved in industrial automation, predictive maintenance, and intelligent sensing applications.<h3>The Role of Portenta X8 in Edge AI Deployments  <iframe id="635a948941249700089f8799" width="250" height="440" src="https://wevolver.com/iframe/specs/arduino-portenta-x8?iframe=true" frameborder="0" scrolling="no"></iframe></h3>For developers looking for a hardware platform that efficiently bridges embedded and industrial applications, the <a href="https://www.arduino.cc/pro/hardware-product-portenta-x8/" target="_blank">Arduino Portenta X8</a> offers a compelling solution. With its combination of embedded Linux and microcontroller capabilities, it provides:<ul><li>Multi-Core Processing: A powerful heterogeneous architecture that enables real-time AI inference while handling additional processing tasks.</li><li>Linux Compatibility: The ability to run containerized applications seamlessly, offering flexibility for edge AI implementations.</li><li>Connectivity Options: Built-in support for Wi-Fi, Bluetooth, and Ethernet, essential for real-time data transmission and remote management.</li></ul>By leveraging the Portenta X8’s capabilities alongside Docker, developers can streamline the deployment of machine learning models while maintaining security, scalability, and performance at the edge.<h3><iframe width="640" height="360" src="https://www.youtube.com/embed/c2nz1wLIp5g?&amp;wmode=opaque&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0"></iframe></h3><h3>Get Started with the Full Guide</h3>To deploy your own Edge Impulse models using Docker, explore the step-by-step tutorial <a href="https://docs.arduino.cc/tutorials/portenta-x8/edge-ai-docker-container/" target="_blank">here</a> and begin integrating intelligence into your edge devices today.

The Arduino Portenta X8 combines embedded Linux and microcontroller capabilities.

Deploying machine learning models at the edge is crucial for real-time data processing in industrial applications. This article explores how Docker simplifies Edge AI deployment on the Arduino Portenta X8, enabling scalable and secure AI implementations on embedded Linux systems.

Deploying Edge AI Models with Docker Containers

<html><head></head><body>Event-based computing is transforming AI processing, delivering smarter, faster, and hyper-efficient solutions across industries like IoT, healthcare, and automotive. It enables smart assistants to respond to user commands intelligently without excessive battery drain and extends battery life in essential wearable devices that continuously monitors your voice, pulse, stride, breathing, activity and surroundings. As technology evolves, event-based computing paves the way for more sustainable and innovative applications in everyday life.Spearheading this transformation, <a href="https://brainchip.com/">BrainChip</a>, a pioneer in event-based AI, has recently introduced their Akida™ Pico solution. Unveiling a revolutionary ultra-low power co-processor designed to fully leverage the capabilities of event-based architectures.&nbsp;BrainChip’s <a href="https://brainchip.com/akida-pico-announcement/">Akida Pico</a> stands out by operating on less than a single milliwatt of power, enabling AI functionality in energy-and size-constrained devices, like wearables, sensor hubs, and IoT devices. By directly processing data streaming from the sensor, Akida Pico reduces reliance on the cloud, delivering substantial advantages in latency, privacy, and energy efficiency.<h2 dir="ltr">Why Event-Based Computing Matters in the Real World</h2>Standard approaches to AI processing often involve processing large amounts of data, much of which may be irrelevant to the task at hand. This inefficiency consumes significant energy and introduces latency, which can be detrimental in scenarios requiring real-time responsiveness. Event-based computing flips AI processing on its head, analyzing only essential inputs as they occur. Inspired by neural processing in the brain, event-based architectures focus on temporal patterns, enabling intelligent systems to respond faster and with far greater energy efficiency.&nbsp;The practical implications of event-based computing and Akida Pico’s capabilities are far-reaching. By focusing on temporal patterns and relevant events, this approach is especially effective in:&nbsp;<ul><li dir="ltr">Voice and Audio Processing: Event-based computing excels at tasks such as voice wake detection, keyword spotting, speech noise reduction, and audio enhancement, where responsiveness and low power consumption are critical.</li><li dir="ltr">Continuous Monitoring: For applications like presence detection and motion tracking, Akida Pico ensures efficient, real-time analysis without the need for constant power-hungry processing.</li><li dir="ltr">Edge AI for Wearables: By delivering personalized insights on minimal energy budgets, Akida Pico supports next-generation wearable devices in healthcare, fitness, and consumer electronics.</li></ul><h2 dir="ltr">Redefining AI Performance for Today’s Applications</h2><h3 dir="ltr">Core Features of Akida Pico:</h3><ul><li dir="ltr">Event-Based Data Processing:&nbsp;The architecture focuses on processing relevant events, reducing unnecessary computations and conserving energy.</li><li dir="ltr">Ultra-Low Power Consumption: Consuming less than 1mW, Akida Pico is ideal for battery-powered devices requiring extended operation times.</li><li dir="ltr">Compact Design: A minimal die area and configurable data buffer allow integration into space-constrained designs without compromising functionality.</li><li dir="ltr">False Alarm Filtering:&nbsp;The co-processor’s neural networks filter out non-critical events, preserving power and reducing system wake-ups.</li><li dir="ltr">Developer-Friendly Integration:&nbsp;BrainChip’s MetaTF™ development environment supports TensorFlow, Keras, and PyTorch workflows, simplifying the development and deployment of Temporal-Enabled Neural Networks (TENNs).</li></ul><h2 dir="ltr">Accelerating Development with MetaTF</h2>A key enabler of Akida Pico’s success is the MetaTF software framework, which allows developers to easily compile and optimize AI models for event-based processing. By supporting popular tools like TensorFlow and PyTorch, MetaTF lowers the learning curve for engineers, enabling faster prototyping and deployment of edge AI solutions.MetaTF supplies tools to convert a model with floating point weights and activations to a model with low bit-width weights and activations while maintaining model performance. These tools also convert quantized models trained using traditional deep learning methods to event-domain models for execution with low-latency and low-power on the Akida Pico Processor.&nbsp;<h2 dir="ltr">Expanding Possibilities of Edge AI Across Industries</h2>Event-based computing is driving innovation across multiple sectors, and the Akida Pico is positioned to lead the charge. Its ability to deliver high performance with ultra-low power consumption makes it a valuable asset in:<ul><li dir="ltr">Industrial IoT:&nbsp;Continuous monitoring of equipment and infrastructure is more efficient and cost-effective with Akida Pico’s energy-saving capabilities.</li><li dir="ltr">Automotive Systems: From early detection systems to low-latency decision-making, Akida Pico enhances the functionality of AI-driven automotive solutions.</li><li dir="ltr">Defense and Security:&nbsp;The co-processor’s real-time event detection and low power requirements are critical for mission-critical systems that operate in resource-constrained environments.</li></ul><h3 dir="ltr">Explore the Future of Edge AI</h3>BrainChip provides a range of software, hardware and IP products that can be integrated into existing and future designs, with a roadmap for customers to deploy multi-modal AI models at the edge.<hr><span style="font-size: 11pt; font-family: Arial, sans-serif; background-color: transparent; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; font-variant-position: normal; font-variant-emoji: normal; vertical-align: baseline; white-space-collapse: preserve;">Discover&nbsp;<a href="https://brainchip.com/akida-foundations/"><span style="font-size: 11pt; font-family: Arial, sans-serif; color: rgb(17, 85, 204); background-color: transparent; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; font-variant-position: normal; font-variant-emoji: normal; text-decoration-line: underline; text-decoration-skip-ink: none; vertical-align: baseline; white-space-collapse: preserve;">more about their Akida series</a><span style="font-size: 11pt; font-family: Arial, sans-serif; background-color: transparent; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; font-variant-position: normal; font-variant-emoji: normal; vertical-align: baseline; white-space-collapse: preserve;">&nbsp;and how the&nbsp;<a href="https://brainchip.com/akida-pico-announcement/"><span style="font-size: 11pt; font-family: Arial, sans-serif; color: rgb(17, 85, 204); background-color: transparent; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; font-variant-position: normal; font-variant-emoji: normal; text-decoration-line: underline; text-decoration-skip-ink: none; vertical-align: baseline; white-space-collapse: preserve;">Akida Pico Ultra Low Power</a><span style="font-size: 11pt; font-family: Arial, sans-serif; background-color: transparent; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; font-variant-position: normal; font-variant-emoji: normal; vertical-align: baseline; white-space-collapse: preserve;">&nbsp;can elevate your projects in speech, audio, and medical monitoring.</body></html>

Event-based computing is transforming AI processing, delivering smarter, faster, and hyper-efficient solutions across industries like IoT, healthcare, and automotive.

Smarter, Faster, Hyper-Efficient: Why Event-Based Computing is Key to AI at the Edge

<html><head></head><body>It’s no secret that Edge AI is a hot topic, arguably the main theme of last week's Embedded World&nbsp;in Nuremberg, Germany.The biggest buzz on the conference floor?&nbsp;<a href="https://www.linkedin.com/company/edgeimpulse/" target="_blank">Edge Impulse</a>'s acquisition by&nbsp;<a href="https://www.linkedin.com/company/qualcomm/" target="_blank">Qualcomm</a>! Would this mean that the technology is moving beyond its infancy and into industry adoption?<h3>The Edge AI space is moving fast</h3>“This year’s insane!” cried&nbsp;<a href="https://www.linkedin.com/in/zachshelby" target="_blank">Zach Shelby</a>, reacting to the sheer number of real Edge AI projects on display. While Edge Impulse may be ahead of the curve, it’s clear that the Edge AI space is moving fast. A year ago, a conversation about “the state of Edge AI” might have felt like an echo chamber where people could only talk about a handful of Edge AI applications which barely moved beyond its POC stage. This year is different. It seemed like every exhibitor at Embedded World had a real Edge AI story to share, primarily focused on computer vision, built without powerful, energy-draining and expensive GPUs. Instead, exhibitors showcased low-resolution cameras connected to inexpensive, battery-powered microcontrollers, running ultra-fast models with minimal latency and energy consumption<h3>Real data, real sensors</h3>The Edge AI community is flourishing, with more developers than ever gaining access to free AI models. But the real bottleneck to adoption isn’t the models,&nbsp;it’s the quality of the data.Companies have been collecting data for decades, yet much of it can be completely thrown away. Many data sets are sampled, aggregated or lack clear context on what happened when that data was collected, making them useless for training AI models. “We need good data from real sensor as close to the real deployments as possible.” Shelby underscores the need for best practices to enable people to collect high quality data and more collaboration centered around data; we need to find better ways to crowdsource, collect and share data.<h3>We don’t need more chatbots!</h3>The threshold to getting started might seem low, but we’re mostly talking to the same group of embedded developers who’re actually putting Edge AI to use. “It’s an engineering party, hosted in an ivory tower” as&nbsp;<a href="https://www.linkedin.com/in/fabioviolante/" target="_blank">Fabio Violante</a>, CEO of&nbsp;<a href="https://www.linkedin.com/company/arduino/" target="_blank">Arduino</a> put it. We need to open up, bringing in more creativity.“What’s the hello world of AI?” asked&nbsp;<a href="https://www.linkedin.com/in/bsatrom" target="_blank">Brandon Satrom</a> from&nbsp;<a href="https://www.linkedin.com/company/buildwithblues/" target="_blank">Blues</a>. “It’s chatbots!” And if there’s anything we don’t need more of, it’s chatbots, which won’t work on MCU-based devices anyway. Put that Raspberry Pi away and stop running Ollama. Look beyond chatbots and start implementing sensor-driven applications which solve real problems. As Violante nicely put it “Edge AI is there to solve problems, not the other way around”<h3>Community, unite!</h3>The industry is at a turning point with big shots investing heavily. Now is the time to define best practices, share experiences and crowdsource high quality data. Could this be the moment for the&nbsp;<a href="https://www.linkedin.com/company/edgeaifoundation/" target="_blank">EDGE AI FOUNDATION</a> to take the lead?Let’s stop messing around and start solving real problems using a technology that has proven itself. Laurens SlatsBased on the conversation with <a href="https://www.linkedin.com/in/bernardpete" target="_blank">Pete Bernard</a>, <a href="https://www.linkedin.com/in/zachshelby" target="_blank">Zach Shelby</a>, <a href="https://www.linkedin.com/in/bsatrom" target="_blank">Brandon Satrom</a>, and <a href="https://www.linkedin.com/in/fabioviolante" target="_blank">Fabio Violante</a>.<footer><a href="https://iotstars.com/" target="_blank"></a></footer></body></html>

The State of Edge AI – panel at IoT Stars

The Edge AI industry is at a turning point with big shots investing heavily. Now is the time to define best practices, share experiences and crowdsource high quality data. Who will be taking the lead in guiding the industry forward?


Big shots are joining the Edge AI community

<html><head></head><body>Bringing AI to the edge is a complex process filled with trade-offs in performance, power efficiency, and cost. Developers often face long development cycles, challenging optimizations, and fragmented hardware compatibility. How can companies accelerate edge AI innovation while reducing time-to-market?In this webinar, host Smair Jaber is joined by Eric Smiley, VP of Business Development at embedUR, and John Marconi, VP of Technology and Architect at embedUR, for an insightful conversation on how ModelNova, embedUR’s AI acceleration platform, is transforming edge AI development. Discover how developers can move from proof-of-concept to deployment in record time by leveraging pre-optimized AI models, curated datasets, and AI-ready blueprints designed for resource-constrained edge devices.&nbsp;<iframe id="JotFormIFrame-250683743144458" title="Edge AI 2025 - embedUR Registration (Embedded)" allowtransparency="true" src="https://form.jotform.com/250683743144458" frameborder="0" style="min-width:100%;max-width:100%;height:420px;border:none;" scrolling="no" 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><h3 dir="ltr">In this webinar, we’ll explore:</h3><ul><li dir="ltr">How ModelNova slashes optimization time from weeks to minutes</li><li dir="ltr">How pre-trained AI models and curated datasets eliminate the need for extensive training and adaptation</li><li dir="ltr">The biggest trade-offs in edge AI (power, performance, and cost) and how to navigate them effectively</li><li dir="ltr">How embedUR’s collaborations with silicon vendors ensure seamless hardware integration</li><li dir="ltr">Real-world applications where ModelNova accelerates deployment, from face recognition and people detection to audio de-noising</li></ul><h3 dir="ltr">Event details</h3>Webinar Date:&nbsp;May 6th 2025 | 6 pm CET | 9:00 PSTWebinar Duration: 1 hour. 45 minutes discussion with 15 minutes for QnA.&nbsp;<h3 dir="ltr">About the speakers</h3>Eric Smiley,&nbsp;VP of Business Development at embedUR.Bridging ideas with technology, Eric helps connect embedUR’s expertise with customers and partners to envision, design, develop and support tomorrow’s intelligent connected solutions. &nbsp;These innovative products help us communicate, educate, and innovate even further. Eric believes in the power of spreading knowledge and ideas through technology that simplifies our lives and facilitates connections.&nbsp;Studying at Duke University, Eric earned undergraduate and master's degrees in electrical engineering fostering a passion for education and entrepreneurship. &nbsp;He co-founded a Wi-Fi security start-up to help secure wireless connectivity. Eric continued his career at Hatteras Network as a QA and Sales Engineer gaining Carrier networking deployment experience.The service provider knowledge led to an opportunity with a dark fiber service provider to design, deploy and manage networks for School districts. Eric then joined LVL7 Systems as a Field Applications Engineer helping to market Switching and Wireless software solutions for embedded networking products. When Broadcom acquired LVL7, Eric stepped into a Product Line Management role assisting with the formation and rapid growth of Broadcom’s Enterprise Wi-Fi group.For over a decade, Eric has worked as part of embedUR’s uniquely experienced team riding the latest technology waves from Wi-Fi to Cloud to IoT and now to Edge AI, helping our customers utilize advanced innovations in their new solutions. John Marconi,&nbsp;VP of Technology at embedUR.John is a seasoned technology leader with over 25 years of experience in designing, developing, and launching innovative products. He has played a pivotal role in creating award-winning software solutions, bridging low-level device drivers, embedded systems, and scalable cloud architectures with big-data and edge AI innovations. At embedUR, he leads the technology and architecture across the company including embedUR's innovative ModelNova platform and all edge AI initiatives. &nbsp;Passionate about empowering customers to turn their concepts into market-ready products, John's vision continues to drive technological progress.<h3 dir="ltr">About the host</h3>Samir Jaber, editor, writer, and industry expert on topics of technology, science, and engineering.&nbsp;He is the editor-in-chief of the 2024 State of Edge AI and 2023 Edge AI Technology reports with Wevolver. Samir is the Chief Editor and Founder of Wryters, a content marketing and consulting agency. He has comprehensive experience working with Fortune 500 companies and industry leaders as a writer, editor, content manager, and consultant. He is an online content specialist with an academic background in mechanical engineering, nanotechnology, and scientific research. Samir is also 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. His experience includes award-winning engineering research and patented engineering design in the fields of nanofabrication and microfluidics.</body></html>

Register for our latest Edge AI webinar | May 6, 2025 06:00 PM CET

From Concept to Deployment: How embedUR's ModelNova Accelerates Edge AI Development

The future of healthcare is shifting beyond hospital walls, bringing intelligent, power-efficient health monitoring solutions directly into patients’ daily lives. But making remote patient monitoring (RPM) truly effective requires overcoming major barriers: battery life, device size, and data privacy. In this webinar, host Samir Jaber is joined by Carlos Morales, VP of AI at Ambiq, in a discussion on how Ambiq’s proprietary Subthreshold Power Optimized Technology (SPOT®) and ultra-low-power semiconductor solutions are pushing the envelope on body-worn AI and RPM solutions. Learn how Ambiq is tackling industry-wide challenges, enabling always-on health monitoring without compromising comfort, security, or battery life. <iframe id="JotFormIFrame-250563172046452" src="https://form.jotform.com/250563172046452" frameborder="0" style="min-width:100%;max-width:100%;height:400px;border:none" scrolling="no">     </iframe> <h3 id="isPasted">In this webinar, we’ll explore:</h3><ul><li>The power-efficiency breakthrough that allows body-worn AI devices to run longer and process real-time health insights at the edge</li><li>Privacy-first AI innovations, including millimeter-wave radar and local data processing, that minimize reliance on cloud connectivity</li><li>Real-world applications of Ambiq’s technology, from fall detection and heart rate monitoring to advanced hearing aids and early dementia detection</li><li>What’s next for edge AI in digital health: how ultra-low-power AI is shaping the future of personalized care and smart health ecosystems</li></ul><h3 id="isPasted">Event details</h3>Webinar Date: Apr 8, 202 | 6 pm CET | 9:00 PSTWebinar Duration: 1 hour. 45 minutes discussion with 15 minutes for QnA. <h3>About the speakers:</h3>Carlos Morales, VP of AI at AmbiqCarlos Morales is the current Vice President of Artificial Intelligence (AI) at Ambiq, where he leverages the Subthreshold Power Optimized Technology (SPOT®) platform to advance AI across extensive mobile and portable devices within the IoT universe. His strategic focus includes pioneering low-latency, AI-driven applications while minimizing energy consumption by implementing local and intelligent analytics.Bringing over three decades of research and development experience from silicon to cloud, Morales has a distinguished career marked by significant technological innovations. Before his role at Ambiq, he spearheaded a comprehensive AI strategy and business development with Intel®'s global AI Software Team, where he served for five years. Earlier in his career, Morales was instrumental in Cisco’s Advanced Architecture and Research group, where he led initiatives such as the development of Fog Computing and the creation of the first full-featured software-defined router, now crucial in Edge to Cloud operations.Morales enthusiastically describes his decision to join Ambiq: “The moment I understood Ambiq’s unique value, I knew I had to be part of their journey. The opportunities unlocked by bringing AI to the vast far-edge ecosystem are mind-boggling and terrifically exciting. Ambiq is uniquely positioned to transform the computing landscape, and I am thrilled to contribute to this revolutionary path.”<h3>About the host: </h3>Samir Jaber is an editor, writer, and industry expert on topics of technology, science, and engineering. He is the editor-in-chief of the 2024 State of Edge AI and 2023 Edge AI Technology reports with Wevolver. Samir is the Chief Editor and Founder of Wryters, a content marketing and consulting agency. He has comprehensive experience working with Fortune 500 companies and industry leaders as a writer, editor, content manager, and consultant. He is an online content specialist with an academic background in mechanical engineering, nanotechnology, and scientific research. Samir is also 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. His experience includes award-winning engineering research and patented engineering design in the fields of nanofabrication and microfluidics.

Register for our latest Edge AI webinar  |  Apr 8, 2025 06:00 PM CET. 

Powering the Future of Digital Health: Ultra-Low-Power AI for Smarter Patient Monitoring

Reliable and efficient cashierless stores are becoming increasingly in demand as they save customers’ time and energy and create unprecedented, customer-friendly shopping experiences. Additionally, with the global shortage of service employees, integrating the advancements of artificial intelligence (AI) and machine learning (ML) for significant transformation in the retail industry is crucial for retailers to meet customer expectations. AI technologies can improve efficiency, personalize customer experiences, cost-savings, forecast demand, and streamline operations in the retail industry.According to a 2023 report by Grand View Research, market researchers expect the global AI retail market to reach over $40 billion by 2030, growing at a compound annual growth rate (CAGR) of 23.9 percent. This quick expansion demonstrates a significantly growing reliance on AI in retail [1]. &nbsp;This is where Axelera AI can be a game-changer for the retail industry, offering innovative solutions that enhance both the customer experience and store operations.&nbsp;From enabling cashierless shopping to optimizing inventory and personalizing the shopping journey, Axelera AI empowers retailers to meet the demands of modern consumers. It enables a smooth shopping experience with intelligent tracking, immediate payment processing, customized shopping, and improved security. These advantages can help eliminate traditional checkout processes.<h2 dir="ltr">Cashierless Shopping: The Axelera AI Advantage</h2>AI-powered systems use a combination of computer vision, sensor fusion, and deep learning to track products chosen by customers. These products are then automatically charged to customers' accounts as they leave the store. Such systems enable customers to shop and check out without traditional cashiers, reducing wait times and improving convenience. Here are some of the features implemented in cashierless shopping:<ul><li dir="ltr">Biometric authentication or QR Code Scanning:&nbsp;Consumers may use multiple authentication methods or even QR code scanning for payment methods and identification. This biometric authentication is based on innovative AI technologies like computer vision and deep learning algorithms, resulting in advanced security, operational efficiency, and frictionless transactions.</li><li dir="ltr">Automated Purchase Tracking:&nbsp;AI-powered systems automatically track customer movements, recognize selected products, count purchases, and charge the associated payment method. Such an automated checkout process helps optimize operations and increase customer satisfaction by reducing checkout time, enhancing accuracy, and improving inventory visibility.</li><li dir="ltr">Preventative Measures Against Loss, Theft or Shrinkage:&nbsp;An AI-driven, computer-vision-enabled Point of Sale (POS) system can help prevent loss or theft.&nbsp;Advanced AI-powered surveillance systems can observe in-store activity, detect suspicious behavior, and provide improved security.</li></ul>Axelera AI's powerful processing capabilities make it ideal for running sophisticated computer vision algorithms in real time. In a cashierless store, cameras connected to an Axelera AI board can track customers as they move through the store, identifying the items they pick up and automatically adding them to their virtual cart. When customers are ready to leave, the system automatically charges their account for the items in their cart, eliminating the need for traditional checkout lines. This creates a seamless shopping experience for the customers.<h2 dir="ltr">Personalized Shopping Experiences</h2>In retail, the speed and smoothness of shopping are valuable aspects, but they do not represent the full picture. There is more to the shopping experience, including enjoyability, comfort, satisfaction, and easier decision-making. This is where personalization plays a central role.AI-driven personalization makes shopping more relevant and engaging, resulting in holistically better shopping experiences. For instance, AI helps retailers gather and examine vast amounts of consumer data, including past purchases and behavioral trends. This enables them to provide better services and customizable experiences based on these datasets. Customer data and insights driven by AI play a significant part in developing individualized shopping experiences.Similarly, stores can generate real-time dynamic, personalized experiences with AI-powered smart devices and sensors. By monitoring consumer movements and actions within the store, these technologies enable retailers to generate product recommendations and send them directly to customers’ smartphones.&nbsp;A 2024 IBM consumer study surveyed 20,000 consumers across 26 countries and found that 85 percent of Gen Z consumers, 89 percent of millennials, and 75 percent of baby boomers and seniors prefer personalized offers[2]. This is a strong indication of the need for “hyper-personalization.”With Axelera AI, retailers can implement dynamic pricing strategies based on real-time data to provide customized promotions depending on variables like stock levels, customer behavior, and time of day. &nbsp;As a result, they can improve customer engagement, maximize revenue, and maintain their competitiveness in the market.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1740768787141-walmart.jpg" style="width: 100%px;" class="fr-fic fr-dib">Walmart will have electronic pricing in all its stores by 2026. Image credit: Walmart.For instance,&nbsp;Axelera AI can help a clothing store gather important information about its in-store customers. When a customer tries on a certain jacket several times without buying it, the store can use Axelera AI technology to track that behavior and set up personalized activities to help the customer make a buying decision. These include advertising the product features further or giving the customer a discount.<h2 dir="ltr">Enhancing Store Operations</h2>Integrating AI into store operations through smart shelves, automated checkout systems, and in-store navigation provides customers with a more efficient, convenient, and personalized shopping experience.Smart shelves, integrated with AI, sensors, and RFIDs, can streamline store operations, improve inventory management, and significantly enhance customer experience. AI can track inventory in real time, alerting staff when stock is low. According to a 2024 report by Deloitte, retailers using smart shelves have seen more streamlined and efficient restocking by leveraging robotics and connected devices[3]. Similarly, automated checkout systems that use AI enable cashierless transactions, reduce wait times, minimize human error, and enhance customer satisfaction while optimizing labor costs for retailers.Axelera AI enables continuous monitoring of shelves, detection of stock levels, and tracking of product movement. It analyzes sales data and other factors to predict demand and optimize stock levels, reducing overstocking and waste. It also provides retailers with actionable insights into inventory management, enabling data-driven decisions and strategic planning.For example, in a supermarket, Axelera AI can monitor the shelves and send an alert to restock items when inventory falls below a certain threshold. This would prevent out-of-stock situations and improve customer satisfaction.In addition, with real-time mapping and location-based services, AI-driven in-store navigation systems can enhance store operations by improving customer experience, reducing employee workload, enhancing inventory management, and boosting sales. Statistics show that 59 percent of consumers want to use AI applications while shopping, and over 80 percent of those who have not used AI yet would like to learn more about how it can help them shop better[2].<h2 dir="ltr">Integrating Online and Offline Experiences</h2>By integrating online and offline data, AI creates a seamless, personalized customer experience that meets today’s high expectations. As more businesses embrace AI-driven strategies, the gap between digital and physical interactions will continue to narrow, leading to more engaged customers and successful marketing outcomes. AI can fill the gap between online and offline, providing customers with a smooth, engaging, and customized shopping experience.<h2 dir="ltr">Conclusion</h2>As retailers use AI and innovative technologies to create a more engaging, personalized, and convenient shopping experience, the future of cashierless shopping sounds very promising. Axelera AI solutions can revolutionize the retail sector by providing innovative features that improve store operations and customer satisfaction. With retail becoming an ever-more demanding space, integrating AI-driven technologies like Axelera AI boards will be crucial in delivering the seamless, efficient, and engaging experiences that shoppers expect.<h2 dir="ltr">References</h2>[1]<a href="https://www.grandviewresearch.com/industry-analysis/ai-retail-market-report">&nbsp;Artificial Intelligence in retail market trends | Grand View Research</a>[2]<a href="https://www2.deloitte.com/content/dam/Deloitte/in/Documents/consumer-business/in-cb-future-of-retail-profitable-growth-through-technology-and-AI-noexp.pdf" data-lf-fd-inspected-kn9eq4roawkarlvp="true">&nbsp;</a><a href="https://www.mytotalretail.com/article/ai-powered-retail-revolutionizing-in-store-experiences-to-meet-modern-consumer-demands/">AI-Powered Retail: Revolutionizing In-Store Experiences to Meet Modern Consumer Demands</a>&nbsp;[3]<a href="https://www2.deloitte.com/content/dam/Deloitte/in/Documents/consumer-business/in-cb-future-of-retail-profitable-growth-through-technology-and-AI-noexp.pdf" data-lf-fd-inspected-kn9eq4roawkarlvp="true">&nbsp;Future of retail: Profitable growth through technology and AI | Deloitte</a>

AI and ML-driven cashierless stores not only improve operational efficiency and customer personalization but are also expected to propel the global AI retail market to over $40 billion by 2030. 

Transforming Retail AI: The Future of Cashierless Shopping

From physical threats to cyberattacks, security challenges are growing more complex. Yet, AI is reshaping the way we protect people, infrastructure, and digital assets by enabling real-time threat detection, intelligent surveillance, and advanced access control. This AI-powered security leverages deep learning, computer vision, and edge AI to make security systems faster, smarter, and more reliable.&nbsp;In this context, <a href="https://www.axelera.ai/">Axelera AI </a>covers a wide range of computer vision and other AI use cases with its innovative AI inference hardware and software platform for segmentation, pose estimation, image classification, and object detection, to name a few. It is low-cost and uses low power while offering excellent performance, efficiency, and usability. With its Metis AI Processing Unit (AIPU), Axelera AI is pioneering this shift to AI security by delivering high-performance, power-efficient AI inference for applications such as intelligent video analytics, biometric authentication, and perimeter security.&nbsp;This article explores how AI is redefining security to be more proactive, adaptive, and resilient against modern threats and shows how Axelera AI helps transition to a technologically advanced, safe, ethical, and sustainable future for all.<h2 dir="ltr">Video Surveillance and Monitoring</h2><h3 dir="ltr">Intelligent Video Analytics</h3>AI for video surveillance refers to the application of AI to enhance safety and security for people and assets. Utilizing computer vision, security professionals can identify various elements, such as objects, vehicles, people and/or their actions, or specific events. Intelligent video analytics is a technology that analyzes video footage and produces actionable insights by utilizing computer vision and AI. Some primary advantages of intelligent video analytics are increased operational efficiency, enhanced staff and visitor management, improved marketing, and better customer experience.Three key capabilities enable intelligent video analytics:<ol><li dir="ltr">Object recognition&nbsp;is one of the most important computer vision tasks and involves recognizing and categorizing objects in digital images and video.</li><li dir="ltr">Anomaly detection&nbsp;is the identification of abnormal events in video surveillance applications and is automated through various machine learning and deep learning techniques.</li><li dir="ltr">Pattern recognition&nbsp;is the process of using techniques and algorithms to find and examine recurrent patterns in video data.</li></ol><h3 dir="ltr">Revolutionizing Surveillance with Intelligent Video Analytics</h3>The Axelera AI inference card, powered by the Metis AIPU, is redefining the potential of intelligent video analytics by processing high-resolution video streams in real time, making it an ideal solution for advanced surveillance systems. Its ability to utilize cutting-edge AI models for tasks such as object detection, pose and action estimation, and anomaly detection ensures that monitoring is both automated and highly accurate. These capabilities provide security teams with actionable insights and faster response times, enhancing safety and operational efficiency. Additionally, because Axelera AI processes data at the edge, it reduces dependency on cloud infrastructure, lowering bandwidth costs and energy consumption and contributing to a more sustainable security approach.For instance, in a bustling urban environment, surveillance cameras powered by an Axelera Metis AIPU can continuously scan for unusual activities, unauthorized access, or potential threats, alerting security personnel immediately. The advantage of this approach is real-time identification that does not rely solely on a human watching dozens of camera streams. By leveraging parallel processing, the board accelerates AI inference, enabling the rapid analysis of large video streams and facilitating real-time decision-making. This ensures that user demands for speed and reliability are met without significant increases in costs or reductions in performance.&nbsp;<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1740766484978-axelera-metis-chip.jpg" style="width: 100%px;" class="fr-fic fr-dib">The Metis AI Processing Unit (AIPU) is built for computer vision inference on the edge, with scalable technology to meet the future demands of AI acceleration workloads.Furthermore, since all AI processing occurs on the device, sensitive data like personally identifiable information does not need to be transmitted to the cloud. This edge-based approach strengthens data privacy, making it an ideal solution for organizations operating under stringent regulations. Whether detecting anomalies in crowded spaces or streamlining surveillance workflows, the Metis AIPU is central to intelligent video analytics, shaping a safer and more secure future.<h2 dir="ltr">Access Control Systems</h2>AI improves access control by having a predetermined set of people (employees, residents, etc) that can enter a given area; it can also simplify system management and the access control process. AI-powered access control systems can interface with other security systems, such as video surveillance systems, to enable a more thorough approach to security. In addition to computer vision, access control may also draw on Natural Language Processing (NLP).NLP provides installers and security operators with individualized instructions and guidance to help them perform their jobs more effectively. Trained models use ML to identify patterns and non-obvious relationships in massive amounts of data. These models can then predict potential threats, impending device failures, alarm handling, and other issues.<h3 dir="ltr">Biometric Access Control</h3>AI security algorithms can process large volumes of biometric data in real time, enabling quick and precise identification. With the help of ML algorithms, biometric access control systems adjust and improve over time, increasing their accuracy and dependability. Common families of ML algorithms include the Deep Learning Model, Support Vector Machine (SVM), Convolutional Neural Network (CNN), Recurrent Neural Networks (RNN), Gaussian Mixture Model (GMM), and Principal Component Analysis (PCA). These algorithms can be applied to various biometric modalities, such as facial, fingerprint, iris, and voice recognition, to guarantee AI security.<h3 dir="ltr">Axelera AI: Redefining Biometric Access Control</h3>The Metis AIPU elevates biometric access control systems by significantly improving the speed and precision of identity verification processes. Their ability to process large datasets at the edge efficiently guarantees that access control systems remain both highly secure and operationally effective, even in demanding, resource-limited environments.In a nutshell, the Metis AIPU can enable advanced, on-device AI processing algorithms and enhance biometric access control without requiring data transfers to the cloud. This takes place through high-performance and adaptive AI features that improve real-time decision-making, protect data security and privacy, and use less bandwidth. As a result, this helps cut down on both latency and power consumption, enhancing security while minimizing environmental impact.For example, in a corporate setting, the Metis AIPU can empower access control systems to instantly verify identities granting or denying entry with unparalleled accuracy. This robust approach enhances security for sensitive areas and simplifies the management of access permissions, providing organizations with a future-ready solution for safeguarding their operations.&nbsp;Moreover, Axelera AI’s biometric access control solutions are designed with compliance in mind, ensuring adherence to regional privacy regulations such as GDPR in Europe. By processing biometric data on the edge device rather than transmitting it to the cloud, the Metis AIPU aligns with data minimization principles, enhancing both security and regulatory compliance and making biometric access control systems a reliable component of modern security infrastructure.<h2 dir="ltr">Perimeter Security</h2>Adding real-time computer vision AI to both new and old perimeter security systems enhances the operations, safety, and security of vital facilities and their staff. These include enhanced threat detection and security process simplification, which result in faster and more accurate service response times and lower video infrastructure costs. Some advantages of perimeter security are improved efficiency and accuracy, cost-effective solutions, scalability and flexibility, and enhanced safety.<h3 dir="ltr">Drone Surveillance and Security</h3>The development of AI-driven drone technology represents a significant shift in aerial surveillance capabilities for safety and security applications. AI-powered drones with high-resolution cameras and sophisticated sensors produce precise surveillance data by taking detailed images. This precision is essential for spotting possible security risks and enhancing search and rescue efforts.AI-powered drones increase the effectiveness of surveillance missions in inhospitable regions where human navigation may be difficult. They offer precise surveillance information essential for spotting possible security risks and streamlining search and rescue missions. Autonomous drones can be used for perimeter security in agriculture, construction, energy, surveillance and security applications, delivery services, and even underwater scenarios like inspecting the safety of an offshore oil rig.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1740766571426-shutterstock_2539306533.jpg" style="width: 100%px;" class="fr-fic fr-dib"><h3 dir="ltr" id="isPasted">Boosting Perimeter Security with Axelera AI</h3>The Metis AIPU enables drones to autonomously patrol extensive perimeters, equipped with cameras and sensors capable of detecting and responding to intrusions in real time. This advanced integration of AI allows for precise and efficient monitoring, ensuring swift action when potential threats are identified.For instance, in a large industrial facility, drones powered by Metis can continuously monitor the perimeter, automatically detecting and tracking unauthorized activity. These drones can also play a critical role in disaster response scenarios, providing rapid assessments of damage in challenging environments. By identifying areas requiring immediate attention, they support rescue teams in planning operations more effectively, contributing to improved safety and response efficiency.<h2 dir="ltr">Future Trends</h2>Looking ahead, AI in security is rapidly evolving with multi-modal AI systems that integrate video, audio, and contextual data for comprehensive situational awareness. Emerging technologies such as Vision Transformers (ViTs) and Visual Language Models (VLMs) are redefining how AI understands complex security scenarios without requiring predefined rules. Axelera AI is actively developing solutions that integrate these cutting-edge advancements, ensuring that its hardware remains a future-proof choice for edge AI security applications.Through its&nbsp;<a href="https://www.axelera.ai/metis-aipu">Metis AIPU, Axelera AI</a> offers a transformative approach to security, providing robust, intelligent solutions across a variety of applications. Whether it's enhancing video surveillance, improving access control, or securing perimeters, Axelera AI empowers organizations to protect their assets and ensure the safety of their environments. As security threats continue to evolve, the adaptability and power of Axelera AI make it an essential tool in modern security applications.

AI is transforming security through real-time threat detection and intelligent surveillance, with Axelera AI advancing this shift using its Metis AI Processing Unit (AIPU) to enhance proactive, adaptive, and sustainable security solutions.

Advanced AI Solutions for a Safer World

We are thrilled to share the 2025 Edge AI Technology Report with you.&nbsp;Our most recent comprehensive report delves into the evolution of edge AI from a niche technology to a mainstream powerhouse catalyzing change across autonomous vehicles, IoT, healthcare, and more. From real-time decision-making in autonomous vehicles to immediate patient monitoring in healthcare, edge AI is setting new standards for safety, efficiency, and performance.<h2 dir="ltr">Report highlights:</h2><ul><li data-start="542" data-end="1061" id="isPasted">Technological Enablers of Edge AI: Learn about the the advancements in hardware and software that support edge AI deployment. The report examines innovations in specialized processors and ultra-low-power devices, which are overcoming the limitations of processing power and scalability in resource-constrained environments. This part also explores the integration of lightweight, real-time explainability techniques to enhance transparency and trust in AI decisions.</li><li data-start="1063" data-end="1530">Edge AI's Role in Transforming Industry Operations: understand how edge AI is impacting operational models across industries by enabling real-time analytics and decision-making capabilities. Specific case studies, such as predictive maintenance in manufacturing and real-time patient monitoring in healthcare, illustrate the practical applications and advantages of deploying AI at the data source.</li><li data-start="1532" data-end="1998">Future Technologies and Innovation: The final chapters of the report explore emerging technologies that are likely to impact the future development of edge AI, such as federated learning, quantum neural networks, and neuromorphic computing. It discusses how these innovations will enable more autonomous systems capable of self-learning and real-time adaptation, reshaping competitive dynamics across industries.</li></ul><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1741340294712-5+Rising+Trends+for+AI+Adoption+in+Manufacturing+%281920+x+500+px%29+%281%29.png" style="width: 100%px;" class="fr-fic fr-dib"><h2 dir="ltr"><iframe id="JotFormIFrame-250412152873451" title="Edge AI 2025 - Download Form" allowtransparency="true" src="https://form.jotform.com/250412152873451" frameborder="0" style="min-width: 100%; max-width: 100%; height: 570px; border: none;" scrolling="yes" 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>Report Foreword</h2>What happens when intelligence isn’t just something we access through screens or devices but something embedded in the world around us? When it’s woven into our environments, shaping decisions, and unlocking new ways of working and living?Edge AI is making intelligence feel present—alive in ways we’re just beginning to grasp. It’s shifting AI from something we access to something that moves with us, anticipates needs, and creates new opportunities across industries. Real-time patient monitoring in hospitals, smarter supply chains, and AI-powered creative tools are just a few examples. With this shift comes not only new possibilities but also new responsibilities.In my work at IDEO, I’ve seen how emerging technologies reshape industries and redefine how we interact with the world. Edge AI is shifting the conversation from “How do we use AI?” to “How does intelligence exist around us?”. It’s moving beyond efficiency and automation, becoming something embedded into our environments in ways that feel seamless, responsive, and even alive.Edge AI first gained traction in industries where real-time decision-making was essential. Autonomous vehicles, industrial automation, and healthcare couldn’t afford to rely on cloud processing. What started as a solution for latency, bandwidth, and security challenges is growing into something much larger. Today, it is driving new business models, shaping more intuitive interactions, and transforming everything from adaptive healthcare systems to real-time retail.Hospitals are already using edge AI-powered patient monitoring systems like Biobeat, which track vital signs without needing constant cloud connectivity. In manufacturing, companies like Stream Analyze are embedding AI-driven quality control directly into production lines, reducing defects and improving efficiency. In logistics, P&amp;O Ferrymasters has increased load efficiency by 10% by using AI-driven, real-time tracking and automated decision-making. These aren’t experiments. They are real, present-day innovations that make intelligence more immediate, responsive, and deeply integrated into everyday life.This report comes at a moment when edge AI is shifting from a nice innovation to a foundational layer of technology. From next-generation AI hardware designed for low-power, high-performance edge computing to new breakthroughs enabling generative AI to run on-device, the landscape is shifting rapidly. As the technology evolves, leaders across industries will need to rethink how intelligence is designed, deployed, and experienced. This report offers insights into that transformation.The edge has always been more than just a place where data is processed. It is where intelligence becomes immediate, responsive, and integrated into the world around us. Today, it is also where new ideas, interactions, and possibilities are taking shape.Savannah KunovskyManaging Director of <a href="https://www.ideo.com/emergingtech">IDEO's Emerging Technology Lab</a><h2 dir="ltr">Report Introduction</h2>While people have their eyes on the AI race of language models—from OpenAI’s ChatGPT o1 to DeepSeek’s R1, Anthropic’s Claude 3.5, and Google’s Gemini 2.0—some of the most transformative developments in AI are now occurring at the edge, where immediate, on-site processing is redefining business operations. Dubbed “the era of AI inference,” this <a href="https://www.rcrwireless.com/20250210/ai-infrastructure/convergence-of-test-time-inference-scaling-and-edge-ai">next cycle of AI innovation</a> is shifting inference increasingly onto edge devices, thus enhancing accessibility, customizability, and efficiency in AI applications.With 2025 underway, edge AI is rapidly changing how businesses operate by enabling real-time, localized data processing and decision-making. This shift is fueling significant trends across sectors such as autonomous vehicles, IoT, and computer vision. This report examines the evolution of edge AI from a niche technology to a mainstream driver of industry transformation, combining technical analysis with business insights.The first chapter explores the evolving industry trends driving edge AI adoption. It analyzes how sectors like autonomous vehicles, healthcare, manufacturing, and agriculture are increasingly relying on immediate, localized intelligence to improve safety, operational efficiency, and overall performance. The analysis explains the demand for low-latency processing and reduced bandwidth requirements, setting the stage for a shift in data processing and utilization.The second chapter provides a detailed discussion of how edge AI is transforming operational models across industries. By processing data on-site, businesses achieve real-time analytics and decision-making capabilities that traditional centralized systems cannot offer. Specific applications, such as predictive maintenance in manufacturing and real-time patient monitoring in healthcare, illustrate the advantages of deploying AI directly at the source of data generation.In the third chapter, the focus shifts to the technological enablers that support edge AI deployment. Advancements in specialized processors, ultra-low-power devices, and hybrid edge-cloud frameworks, along with software innovations such as edge-native algorithms and hybrid edge-cloud frameworks, are overcoming the challenges of limited processing power and scalability in resource-constrained environments. Moreover, the chapter addresses the critical topic of explainability in edge AI. By integrating lightweight, real-time explainability techniques, developers can ensure that AI decisions are transparent and verifiable, boosting trust in safety-critical applications and regulatory compliance.The fourth chapter examines the collaborative efforts necessary to build a robust edge AI ecosystem. It explains how hardware vendors, software developers, cloud providers, and regulatory bodies are aligning their strategies to create standardized architectures and interoperable platforms. This section emphasizes the importance of partnerships and shared industry frameworks in ensuring that edge AI deployments are secure, scalable, and sustainable.The final chapter presents a forward-looking perspective on the future of edge AI. It explores emerging technologies such as federated learning, quantum neural networks, neuromorphic computing, and the integration of generative AI models. These innovations will drive the development of autonomous systems capable of self-learning and real-time adaptation, reshaping competitive dynamics across industries.This report promises actionable insights and thought leadership that empower decision-makers with a clear roadmap for harnessing edge AI innovation. Through rigorous analysis and industry-focused reporting, readers will gain a deep understanding of the challenges, opportunities, and practical strategies necessary to lead in the era of localized intelligence.''Samir Jaber - Report Editor''<h2 dir="ltr">About the Report</h2>This report is the latest installment in the Wevolver Edge AI Technology Reports series. It addresses the pressing need for actionable insights into the exploding field of edge AI and strongly focuses on the industry trends that are reshaping various sectors in 2025. It provides a clear view of the challenges and opportunities facing businesses today.&nbsp;Editor-in-chief Samir Jaber led this initiative, drawing on the rigorous research and insights of co-authors John Soldatos and Deval Shah to form a cohesive narrative on the transformative impact of edge AI.&nbsp;The Wevolver team has been instrumental in orchestrating discussions between contributors, synthesizing expert opinions, and steering the focus towards the most pressing questions. This collaborative effort ensures the report not only delivers actionable insights but also advances Wevolver’s mission to equip engineers, developers, and decision-makers with authoritative analysis that catalyzes industry progress.We extend our gratitude to our sponsors, whose generous support has made this insightful exploration possible. Their commitment to advancing edge AI technology underscores the shared vision of fostering a community poised to lead in the era of localized intelligence. This report stands as a testament to the power of partnership and shared knowledge in navigating the future of technology.<hr><h2 dir="ltr">2025 Edge AI Report - Media Partner</h2>Supplyframe transforms how businesses design, source, market, and sell in the global electronics industry. Its intelligence platform, powered by billions of real-time signals, supports 12M+ professionals in driving innovation and optimising $140B+ in annual spending. Learn <a target="_blank" data-stringify-link="https://supplyframe.com/" data-sk="tooltip_parent" href="https://supplyframe.com/" rel="noopener noreferrer" id="isPasted">more</a>.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1741252023378-Media+partner+%281%29.png" style="width: 100%px;" class="fr-fic fr-dib"><hr>

Introduction

Industry Trends Driving Edge AI Adoption

The Role of Edge AI in Transforming Industry Trends

The Technological Enablers of Edge AI

 Building an Edge AI Ecosystem

The Future of Edge AI

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

The 2025 Edge AI Technology Report

In The Rise of Generative AI at the Edge: From Data Centers to Devices webinar,<a href="https://www.linkedin.com/in/bernardpete/">&nbsp;Pete Bernard</a>, Executive Director of the EDGE AI FOUNDATION, joined forces with<a href="https://www.linkedin.com/in/situnayake/">&nbsp;Daniel Situnayake</a> from Edge Impulse and<a href="https://www.linkedin.com/in/marek-poliks-55b2a317b/">&nbsp;Marek Poliks</a> from Particle to dive into the future of AI, all hosted by Samir Jaber.&nbsp;Here’s a quick glimpse into the key highlights from that engaging discussion:<h3 dir="ltr">Generative AI in Refrigerators?</h3>LG’s generative AI-powered fridge at CES won Pete's "WTF Award" for its quirky innovation. Asking your fridge about milk levels may sound futuristic (or maybe unnecessary for some), but it highlights the experimental phase of consumer-focused AI applications.<h3 dir="ltr">Hybrid Cloud-Edge Synergy</h3>The future isn’t about choosing between the cloud and the edge. It’s about integrating their strengths. Hybrid architectures will balance computational power and decentralized capabilities, enabling smarter AI solutions in industries like healthcare and agriculture.<h3 dir="ltr">Overcoming Data Challenges</h3>Lack of training data is a major hurdle for real-world AI deployments. Techniques like zero-shot learning and synthetic data are paving the way for generative AI solutions that work even with minimal initial data.<h3 dir="ltr">What's the Least AI I Can Get Away With?</h3>A key insight from the panel was the importance of not overcomplicating solutions. Efficient, pragmatic engineering should always take precedence over flashy but unnecessary complexity.<h3 dir="ltr">Collaboration is Key</h3>The edge AI ecosystem thrives on partnerships. Collaboration across hardware, software, and industry standards will drive faster adoption, better tools, and more impactful solutions.<h3 dir="ltr">Conclusion</h3>"The Rise of Generative AI at the Edge" webinar provided a comprehensive overview of the current state and exciting future of AI technologies. As companies continue to innovate, the blend of edge computing with generative AI holds the promise of transforming industries by enabling smarter, more efficient, and contextually aware devices. The insights shared during this event underscore a pivotal moment in technology, where the edge becomes a focal point for powerful AI applications that could redefine our interaction with digital systems.

The Rise of Generative AI at the Edge: From Data Centers to Devices Webinar

Missed out latest webinar? Watch it on demand now to stay up to date with all the latest from Generative AI at the Edge.

Generative AI in your fridge and why collaboration is the only way forward. Key insights from The Rise of Generative AI at the Edge Webinar.

In an insightful conversation at CES, Vikram Gupta, Chief Product Officer and SVP GM of IoT Processors at Synaptics, sheds light on the company's groundbreaking collaboration with Google. This partnership aims to revolutionize edge AI technology by integrating Google's open ML core into Synaptics' Astra processor line. Gupta elaborates on the benefits of AI at the edge, such as enhanced security, reduced latency, and lower energy costs, and discusses how context-aware computing is set to transform user interactions with technology across various applications.<h2>Video transcript:</h2>Wevolver: Good to meet you again, Vikram. Could you introduce yourself and tell us about the recent collaboration with Google?Vikram Gupta: Certainly. I'm Vikram Gupta, Chief Product Officer and SVP GM of IoT Processors at Synaptics. Last week, we announced a collaboration with Google, where we are integrating an open ML core from Google into our Edge AI processors in the Astra line. This integration aims to provide a tailored AI solution within our processors, focusing on multimodal, power-efficient applications.Wevolver:&nbsp; Why should AI be implemented at the edge?Vikram Gupta: AI at the edge offers several benefits. It improves response times by processing data locally rather than sending it back to the cloud. This not only enhances user experience but also increases security and privacy. Additionally, it reduces energy costs associated with cloud computing.Wevolver: Can you explain what context-aware computing means for end-users and which devices might see the most enhancements?Vikram Gupta: Context-aware computing enables devices to understand their environment and react accordingly, much like humans do. This capability allows devices to process information locally and respond intelligently, which is crucial for applications like security systems where immediate action is required. It can also help conserve energy by reducing unnecessary operations.Wevolver:&nbsp; What makes the Synaptics Astra AI native platform compelling for edge AI?Vikram Gupta: Launched in April at Nuremberg, the Astra platform is AI-native, designed to fully leverage AI capabilities from the ground up. This approach differentiates our processors by emphasizing AI integration in both hardware and software, enhancing our customers' experience with AI features.Wevolver:&nbsp; What other technologies are you showcasing at CES?Vikram Gupta: Besides our processors, we're highlighting our advancements in wireless technologies under the Veros brand, along with our traditional touch and display technologies used in mobiles, PCs, and automotive products. Our wireless solutions are designed for reliable connectivity and interoperability across devices.Wevolver:&nbsp; What are the next big steps for Synaptics in edge AI and context-aware computing?Vikram Gupta: We're advancing our collaboration with Google, optimizing their ML core for our SoCs. This year, we'll introduce new devices that leverage this technology, catering to a broad spectrum of applications, from wearables to high-power electronics.Wevolver:&nbsp; Thank you for your time, Vikram. Best of luck here at CES.Vikram Gupta: Thank you. It's been great.Learn more about Synaptics <a href="https://www.synaptics.com/">here.</a>

In an interview from CES 2025, Vikram Gupta, Chief Product Officer at Synaptics, discusses the company's strategic collaboration with Google to enhance Edge AI processors, emphasizing the importance of context-aware computing and AI-native platforms for improving user experience and efficiency.

Empowering the Edge: Synaptics' Vikram Gupta on AI Innovation and Strategic Google Collaboration

GPUs excel in parallel processing for graphics and AI training with scalability, while NPUs focus on low-latency AI inference on edge devices, enhancing privacy by processing data locally. Together, they complement each other in addressing different stages of AI workloads efficiently.

NPU vs GPU: Understanding the Key Differences and Use Cases

In the final chapter of the Edge AI Technology Report: Generative AI Edition explores the technical hurdles organizations face as they attempt to leverage edge-based generative AI. It also examines strategic opportunities for innovation in hardware, deployment configurations, and security measures.


Challenges and Opportunities in Edge-based Generative AI

Thank you to everyone that attended the webinar on January 15th. If you missed it, watch the full session below.&nbsp;<iframe width="640" height="360" src="https://www.youtube.com/embed/ONzpXuvVK2Y?&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>In The Rise of Generative AI at the Edge: From Data Centers to Devices webinar,<a href="https://www.linkedin.com/in/bernardpete/" target="_blank" rel="nofollow">&nbsp;Pete Bernard</a>, Executive Director of the EDGE AI FOUNDATION, joined forces with<a href="https://www.linkedin.com/in/situnayake/" target="_blank" rel="nofollow">&nbsp;Daniel Situnayake</a> from Edge Impulse and<a href="https://www.linkedin.com/in/marek-poliks-55b2a317b/" target="_blank" rel="nofollow">&nbsp;Marek Poliks</a> from Particle to dive into the future of AI, all hosted by Samir Jaber.&nbsp;Here’s a quick glimpse into the key highlights from that engaging discussion:<h3 dir="ltr">Generative AI in Refrigerators?</h3>LG’s generative AI-powered fridge at CES won Pete's "WTF Award" for its quirky innovation. Asking your fridge about milk levels may sound futuristic (or maybe unnecessary for some), but it highlights the experimental phase of consumer-focused AI applications.<h3 dir="ltr">Hybrid Cloud-Edge Synergy</h3>The future isn’t about choosing between the cloud and the edge. It’s about integrating their strengths. Hybrid architectures will balance computational power and decentralized capabilities, enabling smarter AI solutions in industries like healthcare and agriculture.<h3 dir="ltr">Overcoming Data Challenges</h3>Lack of training data is a major hurdle for real-world AI deployments. Techniques like zero-shot learning and synthetic data are paving the way for generative AI solutions that work even with minimal initial data.<h3 dir="ltr">What's the Least AI I Can Get Away With?</h3>A key insight from the panel was the importance of not overcomplicating solutions. Efficient, pragmatic engineering should always take precedence over flashy but unnecessary complexity.<h3 dir="ltr">Collaboration is Key</h3>The edge AI ecosystem thrives on partnerships. Collaboration across hardware, software, and industry standards will drive faster adoption, better tools, and more impactful solutions.<h3 dir="ltr">Conclusion</h3>"The Rise of Generative AI at the Edge" webinar provided a comprehensive overview of the current state and exciting future of AI technologies. As companies continue to innovate, the blend of edge computing with generative AI holds the promise of transforming industries by enabling smarter, more efficient, and contextually aware devices. The insights shared during this event underscore a pivotal moment in technology, where the edge becomes a focal point for powerful AI applications that could redefine our interaction with digital systems.<hr>Generative AI is radically changing how we process, analyze, and utilize data, and edge computing is driving this transformation closer to devices and users. To explore this groundbreaking space, join us for The Rise of Generative AI at the Edge: From Data Centers to Devices webinar on Wednesday, January 15th.The webinar will be a fireside chat format with three heavy hitters from the Edge AI landscape.&nbsp;Pete Bernard - Executive Director, Edge AI Foundation Marek Poliks - Technical Success Director, Particle and&nbsp; Daniel Situnayake - Director of Machine Learning, Edge Impulse.<h3 dir="ltr">Event Overview</h3>Date:&nbsp;January 15thTime:&nbsp;18:00 CET, 09:00 PSTDuration:&nbsp;1 hourFormat:&nbsp;Panel Discussion (~45 mins) followed by a Live Q&amp;A (~15 mins)This engaging session will discuss the technological enablers, diverse applications, and profound impact of generative AI at the edge. From real-world applications to tackling current challenges, this webinar offers a unique opportunity to gain actionable insights from leading Edge AI experts. Plus, you'll have the chance to engage directly with the panel during a live Q&amp;A session.<h3 dir="ltr">Why Attend?</h3><ul><li dir="ltr">Explore Cutting-Edge Technology: Learn how Particle's Tachyon 5G single-board computer is revolutionizing intelligent edge applications.</li><li dir="ltr">Hear from Industry Leaders: Gain insights from experts deeply embedded in the intersection of AI, edge computing, and emerging technologies.</li><li dir="ltr">Interactive Q&amp;A: Address your questions directly to our panel of esteemed experts.</li></ul><h3 dir="ltr" id="isPasted">Who Should Attend?</h3>This webinar is ideal for:<ul><li dir="ltr">Engineers, developers, and data scientists eager to harness generative AI at the edge.</li><li dir="ltr">Technology leaders exploring innovative AI-powered solutions.</li><li dir="ltr">Anyone passionate about the intersection of AI, edge computing, and 5G technology.</li></ul><h2>Meet the Host: Samir Jaber</h2>Samir Jaber, representing Wevolver, brings unmatched expertise in technology, science, and engineering communication. As editor-in-chief of major reports like the <a href="https://www.wevolver.com/article/the-guide-to-generative-ai-at-the-edge">Edge AI Technology Report: Generative AI Edition,</a> Samir’s nuanced understanding of AI's technological and societal impacts makes him an ideal host for this dynamic discussion.<h2 dir="ltr">Featured Speakers</h2><ul><li dir="ltr">Daniel Situnayake&nbsp;-&nbsp;Director of Machine Learning, Edge Impulse With career-spanning roles at Google, Tiny Farms, and more, Daniel specializes in advancing machine learning for edge devices. His pioneering work with TensorFlow Lite has empowered developers worldwide.</li><li dir="ltr">Marek Poliks&nbsp;-&nbsp;Technical Success Director, Particle Marek’s multidisciplinary expertise blends AI interface design and deep learning research with practical applications in intelligent device infrastructure. His recent accolades include the Google Art + Machine Intelligence Award.</li><li dir="ltr">Pete Bernard&nbsp;-&nbsp;Executive Director, Edge AI Foundation With over three decades of experience in Silicon Valley and at Microsoft, Pete is at the forefront of edge computing innovation, bridging semiconductors, 5G, and AI through his consultancy, EDGECELSIOR.</li></ul><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzMzODQyMjI4NjEwLVN1YmhlYWRpbmcgKDEwODAgeCAzMDAgcHgpICgxKS5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 100%px;" class="fr-fic fr-dib"><h2 dir="ltr"> </h2>

Join us for The Rise of Generative AI at the Edge: From Data Centers to Devices webinar on Wednesday, January 15th

Discover the Future of AI: Join "The Rise of Generative AI at the Edge" Webinar

Edge AI Technology Report: Generative AI Edition Chapter 3. Advances in model optimization techniques like pruning, quantization, and knowledge distillation pave the way for more efficient deployment of generative AI at the edge. 

Edge AI

Profiles