Uppsala Sweden/San Jose, US; September 27, 2023 – IAR, the world leader in software and services for embedded development, today announced a commercial partnership with Edge Impulse, the leading edge AI platform provider. The partnership is built on integrations between Edge Impulse’s platform and IAR Embedded Workbench, with full integration between the two products on a workflow level.

Engineers building applications with, for example, predictive capabilities can leverage Edge Impulse’s technologies to generate – and evaluate – predictive ML models. These models can be derived from real-time data or previously collected data to test the effectiveness and efficiency of the models. Furthermore, optimized C/C++ code can be generated at any point in the workflow and easily integrated into the embedded application. By leveraging the seamless integration between Edge Impulse’s platform and IAR Embedded Workbench, the engineer saves time (improves time-to-market) and optimizes the ML workload’s code performance.

“The partnership with Edge Impulse puts ML and AI at our customers’ fingertips. It is yet another testament to IAR’s transformation, becoming the solution provider of choice for embedded development organizations, offering a platform where both IAR and our partners bring innovation and additional value to the customer experience”, said Richard Lind, CEO of IAR.

“We’re thrilled to partner with IAR to help embedded engineers deploy AI on the edge quickly and easily with modern, enterprise-grade workflows,” said Zach Shelby, co-founder and CEO at Edge Impulse. “Using Edge Impulse with IAR and Arm® tooling will enable the world’s best ML model efficiency across over 8,700 Arm targets.”

As part of the new business partnership, the tens of thousands of developers worldwide currently using IAR Embedded Workbench will be offered Edge Impulse’s solution as a premium add-on during Q4 2023, starting with introducing the solution to IAR´s current Arm customers.

“Developers are facing increasing software complexity that requires new development flows for optimized ML models,” said Paul Williamson, senior vice president and general manager, IoT Line of Business at Arm. “IAR and Edge Impulse’s partnership will accelerate innovation and offer the ML capabilities required for next generation IoT applications on Arm across areas including industrial automation, smart cities, healthcare and medical.”

About IAR

IAR provides world-leading software and services for embedded development, enabling companies worldwide to create secure and innovative products for today and tomorrow. Since 1983, IAR’s solutions have ensured quality, security, reliability, and efficiency in developing over one million embedded applications for companies across industries such as industrial automation, IoT, automotive and medical. IAR supports 15,000 devices from over 70 semiconductor partners. The company is headquartered in Uppsala, Sweden, and has sales and support offices worldwide. IAR is owned by I.A.R. Systems Group AB, listed on NASDAQ OMX Stockholm, Mid Cap (ticker symbol: IAR B). To learn more, visit www.iar.com.

About Edge Impulse

Edge Impulse offers the latest in machine learning tooling, enabling all enterprises to build smarter edge products. Their technology empowers developers to bring more AI products to market faster and helps enterprise teams rapidly develop industry-specific solutions in weeks instead of years. Edge Impulse provides powerful automations and low-code capabilities to make it easier to build valuable datasets and develop advanced AI for edge devices. Used by makers of health-wearable devices like Oura, Know Labs, and NOWATCH, industrial organizations like NASA, as well as top silicon vendors and over 80,000 developers, Edge Impulse has become the trusted platform for enterprises and developers alike. It provides a seamless integration experience to optimize and deploy with confidence across the largest hardware ecosystem. To learn more, visit edgeimpulse.com.

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“A Neuromorphic Deep Dive into Next-Gen AI Solutions” features technical leads from BrainChip and Edge Impulse offering a detailed exploration of BrainChip’s Akida hardware metrics and providing a framework for designing AI models using Edge Impulse. As part of the webinar, attendees will see live demonstrations of models running on BrainChip AKD1000 reference SoC.

“The world of neuromorphic computing is a truly fascinating one and to some may be perceived as complex,” said Nikunj Kotecha, Senior Solutions Architect at BrainChip. “Through this joint webinar, BrainChip and Edge Impulse will shed more light on how we simplify the development, implementation and deployment of cutting-edge neuromorphic AI models and solutions, to drive innovation in Edge devices. It really is a can’t-miss event.”

Those interested in attending the free webinar can register here.

About BrainChip Holdings Ltd (ASX: BRN, OTCQX: BRCHF, ADR: BCHPY)

BrainChip is the worldwide leader in edge AI on-chip processing and learning. The company’s first-to-market, fully digital, event-based AI processor, Akida TM , uses neuromorphic principles to mimic the human brain, analyzing only essential sensor inputs at the point of acquisition, processing data with unparalleled efficiency, precision, and economy of energy. Akida uniquely enables edge learning local to the chip, independent of the cloud, dramatically reducing latency while improving privacy and data security. Akida Neural processor IP, which can be integrated into SoCs on any process technology, has shown substantial benefits on today’s workloads and networks, and offers a platform for developers to create, tune and run their models using standard AI workflows like Tensorflow/Keras. In enabling effective edge compute to be universally deployable across real world applications such as connected cars, consumer electronics, and industrial IoT, BrainChip is proving that on-chip AI, close to the sensor, is the future, for its customers’ products, as well as the planet. Explore the benefits of Essential AI at www.brainchip.com.

Follow BrainChip on Twitter: https://www.twitter.com/BrainChip_inc
Follow BrainChip on LinkedIn: https://www.linkedin.com/company/7792006

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The Akida platform provides efficient AI edge processing, easily integrated fully digital IP, and a full stack solution. Nayampally showcases demos identifying small objects using theFaster Objects More Objects (FOMO) model from Edge Impulse with a standard camera and counting moving nuts and bolts using a differential vision sensor (DVS) from Prophesee. Finally, he highlights the sensor-agnostic and silicon platform-agnostic attributes, as well as the ease of deployment, of BrainChip’s Akida platform.

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FOMO lets you count objects, find the location of objects in an image, and track multiple objects in real-time using up to 30x less processing power and memory than MobileNet SSD or YOLOv5. Hymel demonstrates how it works using a conveyor belt carrying nuts and bolts, tracking the hardware with a camera-equipped Renesas board. Learn more at https://docs.edgeimpulse.com/docs/edge-impulse-studio/learning-blocks/object-detection/fomo-object-detection-for-constrained-devices.

Edge Impulse is the leading platform for building and deploying machine learning solutions to edge devices. Learn more at www.edgeimpulse.com.

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Equipped with multiple types of sensors that can be used separately or together for sensor fusion, BrickML easily connects to pumps, motors, or other machinery to measure performance and create immediate actionable insights. Designed by Edge Impulse in conjunction with Reloc, BrickML enables enterprises to quickly add valuable machine monitoring capabilities to industrial environments. Learn more at www.edgeimpulse.com.

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Check out the Edge Impulse documentation for more information on how to get started with NVIDIA TAO in your Edge Impulse project!

Get to market faster

The integration of NVIDIA TAO facilitates the building of efficient models faster by combining the power of transfer learning and the latest NVIDIA TAO models. These can be deployed across the entire Edge Impulse ecosystem of devices, silicon, and sensors. This comprehensive integration simplifies the edge AI development process and reduces time-to-market, giving you a critical competitive edge in the rapidly evolving AI landscape.

From MCUs to GPUs: All-in-one edge AI solution

Our all-in-one solution enables you to collect data, train and validate your models, and optimize libraries to run on any edge device. The platform scales from extremely low-power MCUs to efficient Linux CPU targets, GPUs, and NPUs. The result is a seamless, flexible solution tailored to the specific needs of edge AI development.

Fast track to enterprise-grade production

NVIDIA TAO comes packed with over 100 NVIDIA-optimized model architectures, like transformers and fully attentional networks, allowing developers to get a jump-start on their model development. You can quickly fine-tune these models with your own data, enabling a faster, more streamlined development process and a quicker path to enterprise-grade production.

Do more with less data

The integration also enhances data collection from any edge device, improving efficiency and usability. Coupled with Edge Impulse’s auto-labeling tools, you can boost data quality and streamline the labeling process. This allows for robust model training even with less data, reducing resources required and accelerating development.

Optimized for edge devices

Our joint solution allows you to profile the performance of your model on different hardware configurations. You can easily identify the optimal target given your specific use case and hardware constraints. This added level of customization ensures your edge AI solution is perfectly optimized for peak performance.

Collaborate with ease

The Edge Impulse Studio, bolstered by NVIDIA TAO, provides an edge AI development environment that promotes real-time, enterprise-wide collaboration. With an emphasis on team-based development, our platform enables teams with diverse expertise to collaborate from anywhere in real-time. Collaboration has never been easier or more effective.

In conclusion, the integration of NVIDIA TAO into Edge Impulse amplifies our commitment to providing a cutting-edge development platform for the AI sector. Our mission is to help you build robust, high-performance AI models quickly and efficiently, without compromising on customization and flexibility. We can’t wait to see what our developer community will create with this powerful new toolset.

Be sure to visit the Edge Impulse and NVIDIA TAO Toolkit landing page for more information!

Jenny Plunkett
Senior Developer Relations Engineer, Edge Impulse

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

Edge AI, empowered by the recent advancements in Artificial Intelligence, is driving significant shifts in today’s technology landscape. By enabling computation near the data source, Edge AI enhances responsiveness, boosts security and privacy, promotes scalability, enables distributed computing, and improves cost efficiency.

Wevolver has partnered with industry experts, researchers, and tech providers to create a detailed report on the current state of Edge AI. This document covers its technical aspects, applications, challenges, and future trends. It merges practical and technical insights from industry professionals, helping readers understand and navigate the evolving Edge AI landscape.

Report Introduction

The advent of Artificial Intelligence (AI) over recent years has truly revolutionized our industries and personal lives, offering unprecedented opportunities and capabilities. However, while cloud-based processing and cloud AI took off in the past decade, we have come to experience issues such as latency, bandwidth constraints, and security and privacy concerns, to name a few. That is where the emergence of Edge AI became extremely valuable and transformed the AI landscape.

Edge AI represents a paradigm shift in AI deployment, bringing computational power closer to the data source. It allows for on-device data processing and enables real-time, context-aware decision-making. Instead of relying on cloud-based processing, Edge AI utilizes edge devices such as sensors, cameras, smartphones, and other compact devices to perform AI computations on the device itself. Such an approach offers multitudes of advantages, including reduced latency, improved bandwidth efficiency, enhanced data privacy, and increased reliability in scenarios with limited or intermittent connectivity.

“Even with ubiquitous 5G, connectivity to the cloud isn’t guaranteed, and bandwidth isn’t assured in every case. The move to AIoT increasingly needs that intelligence and computational power at the edge.”
– Nandan Nayampally, CMO, Brainchip

While Cloud AI predominantly performs data processing and analysis in remote servers, Edge AI focuses on enabling AI capabilities directly on the devices. The key distinction here lies in the processing location and the nature of the data being processed. Cloud AI is suitable for processing-intensive applications that can tolerate latency, while Edge AI excels in time-sensitive scenarios where real-time processing is essential. By deploying AI models directly on edge devices, Edge AI minimizes the reliance on cloud connectivity, enabling localized decision-making and response.

The Edge encompasses the entire spectrum from data centers to IoT endpoints. This includes the data center edge, network edge, embedded edge, and on-prem edge, each with its own use cases. The compute requirements essentially determine where a particular application falls on the spectrum, ranging from data-center edge solutions to small sensors embedded in devices like automobile tires. Vibration-related applications would be positioned towards one end of the spectrum, often implemented on microcontrollers, while more complex video analysis tasks might be closer to the other end, sometimes on more powerful microprocessors.

“Applications are gradually moving towards the edge as these edge platforms enhance their compute power.”
– Ian Bratt, Fellow and Senior Director of Technology, Arm

When it comes to Edge AI, the focus is primarily on sensing systems. This includes camera-based systems, audio sensors, and applications like traffic monitoring in smart cities. Edge AI essentially functions as an extensive sensory system, continuously monitoring and interpreting events in the world. In an integrated-technology approach, the collected information can then be sent to the cloud for further processing.

Edge AI shines in applications where rapid decision-making and immediate response to time-sensitive data are required. For instance, in autonomous driving, Edge AI empowers vehicles to process sensor data onboard and make split-second decisions to ensure safe navigation. Similarly, in healthcare, Edge AI enables real-time patient monitoring, detecting anomalies, and facilitating immediate interventions. The ability to process and analyze data locally empowers healthcare professionals to deliver timely and life-saving interventions.

Edge AI application areas can be distinguished based on specific requirements such as power sensitivity, size limitations, weight constraints, and heat dissipation. Power sensitivity is a crucial consideration, as edge devices are often low-power devices used in smartphones, wearables, or Internet of Things (IoT) systems. AI models deployed on these devices must be optimized for efficient power consumption to preserve battery life and prolong operational duration.

Size limitations and weight constraints also play quite a significant role in distinguishing Edge AI application areas. Edge devices are typically compact and portable, making it essential for AI models to be lightweight and space-efficient. This consideration is particularly relevant upon integrating edge devices into drones, robotics, or wearable devices, where size and weight directly impact performance and usability.

Nevertheless, edge computing presents significant advantages that weren’t achievable beforehand. Owning the data, for instance, provides a high level of security, as there is no need for the data to be sent to the cloud, thus mitigating the increasing cybersecurity risks. Edge computing also reduces latency and power usage due to less communication back and forth with the cloud, which is particularly important for constrained devices running on low power. And the advantages don’t stop there, as we are seeing more and more interesting developments in real-time performance and decision-making, improved privacy control, and on-device learning, enabling intelligent devices to operate autonomously and adaptively without relying on constant cloud interaction.

“The recent surge in AI has been fueled by a harmonious interplay between cutting-edge algorithms and advanced hardware. As we move forward, the symbiosis of these two elements will become even more crucial, particularly for Edge AI.”
– Dr. Bram Verhoef, Head of Machine Learning at Axelera AI

Edge AI holds immense significance in the current and future technology landscape. With decentralized AI processing, improved responsiveness, enhanced privacy and security, cost-efficiency, scalability, and distributed computing, Edge AI is revolutionizing our world as we speak. And with the rapid developments happening constantly, it may be difficult to follow all the new advancements in the field.

That is why Wevolver has collaborated with several industry experts, researchers, professors, and leading companies to create a comprehensive report on the current state of Edge AI, exploring its history, cutting-edge applications, and future developments. This report will provide you with practical and technical knowledge to help you understand and navigate the evolving landscape of Edge AI.

This report would not have been possible without the esteemed contributions and sponsorship of Alif Semiconductor, Arduino, Arm, Axelera AI, BrainChip, Edge Impulse, GreenWaves Technologies, Sparkfun, ST, and Synaptics. Their commitment to objectively sharing knowledge and insights to help inspire innovation and technological evolution aligns perfectly with what Wevolver does and the impact it aims to achieve.

As the world becomes increasingly connected and data-driven, Edge AI is emerging as a vital technology at the core of this transformation, and we hope this comprehensive report provides all the knowledge and inspiration you need to participate in this technological journey.

Samir Jaber
Editor-in-Chief, Wevolver

This text was contributed to by engineers from Arm, Axelera AI, and BrainChip.

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“Deploy Your Embedded Vision Solution on Any Processor Using Edge Impulse,” A Presentation from Edge Impulse

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“Visual Anomaly Detection with FOMO-AD,” a Presentation from Edge Impulse

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Edge Impulse is thrilled to be returning to the Embedded Vision Summit (EVS) as a premier sponsor and participant. Taking place from May 22nd to May 24th at the Santa Clara Convention Center, it brings 80 industry leaders in vision-based embedded computing along with over 1000 attendees. We can’t wait to show our latest advancements and demonstrate our innovative edge AI solutions.

This marks our fourth year of participation at EVS: two years virtually in 2020 and 2021, and now the second year in person. It’s always a fun and exciting show, with fantastic keynotes (including Edge Impulse CEO Zach Shelby in 2022), great sessions and discussions, awards (Edge Impulse winning for EON Compiler in 2021 and EON Tuner in 2022), and exceptional networking.

Jan celebrates our EON Tuner award at EVS 2022

This year we have a number of engagements at the show. Here’s where you can find us.

Monday, May 22nd: Edge Impulse DevRel engineers Jenny Plunkett and Shawn Hymel will run the Deep Dive session “Create Better Models and Deploy Them Everywhere with Edge Impulse” at 3pm PT in rooms 209/210.

Immediately following that, Edge Impulse is sponsoring the kickoff reception at the convention center’s outdoor terrace.

Tuesday, May 23rd: The exhibits and keynotes officially begin, and co-founder/CTO Jan Jongboom will showcase “Visual Anomaly Detection with FOMO-AD” on the Exhibit Hall ET1 stage at 2:40pm PT.

The exhibit hall will be open for visitors starting at 12:30pm PT. We’ll have a number of tech demos on display that highlight the capabilities of Edge Impulse’s tools, including our nuts and bolts conveyor (using Renesas hardware), Skittles sorter (built with BrainChip hardware), LEGO hard hat detector (using Sony Spresense) a microscope demo (powered by Texas Instruments), and BrickML and Lexmark demos. You can find us in booth #209.

We’ll also have demos throughout the exhibit hall. At the booth of Network Optix, our partner Scailable is showing their integration with Network Optix and demonstrating the seamless deployment of FOMO models, trained using Edge Impulse to Scailable-supported hardware. The demonstration shows an end-to-end solution from model training, to deployment, to integration with the higher level application platform.

You’ll also find an Edge Impulse-powered “superhero classification” demo in the Seeed Studio booth.

At 6pm PT, the Product of the Year Awards will be presented in the exhibit hall, stage ET3. We’ll be in attendance to applaud the winners.

The “Women in Vision” discussion panel will be held at 6:30pm PT on stage ET1.

Wednesday, May 24th: The team will be back on hand to show all the demos and talk more about the ways you can engage with Edge Impulse. The show floor opens at 10am PT.

At 11:25am PT, our global semiconductor business development director Amir Sherman will present on deploying embedded vision solutions to any processor using Edge Impulse. Find him on stage ET1 in the exhibit hall.

The show concludes at 6pm that evening.

We eagerly anticipate the insights and advancements that will come out of EVS this year. As we continue to shape the future of embedded ML technology, we invite you to join us on this journey. See you at the Embedded Vision Summit 2023!

Register now!

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