Eta Compute Standardizes Neural Net-based AI SoCs Around IAR Embedded Workbench Toolchain

January 24, 2019 Brandon Lewis

UPPSALA, SWEDEN. Eta Compute has selected IAR Embedded Workbench for Arm (EWARM) as the preferred toolchain for its machine learning SoCs. By combining EWARM with Eta Compute’s Arm Cortex-M3-based artificial intelligence (AI) processors, developers can maximize power efficiency in machine learning-enabled IoT edge applications.

Eta Compute machine-learning SoCs are based on the company’s DIAL Technology, which enables local machine intelligence on severely resource-constrained devices. IAR EWARM supports development of this technology with the most widely adopted C/C++ compiler and debugger toolchain for Arm Cortex-based applications, as well as integrated code analysis tools and debugging probes.

Eta Compute is part of the Arm DesignStart™ program, which enables the creation of custom SoCs with low cost and fast access, and provides a verified subsystem for a wide range of applications, including IoT, gateways, sensor, control and mixed signal SoCs, as well as design services and support..

“IAR Systems’ tools have a demonstrated capability of generating the smallest, most efficient code while maintaining application integrity,” says Paul Washkewicz, Vice President and Co-Founder of Eta Compute. “IAR Embedded Workbench is fast and lightweight, resulting in an IDE that aids in development.

“Additionally, their quick, knowledgeable, and professional support teams help accelerate the time it takes to move from prototype to production,” he adds.

Eta Compute is a member of the Arm DesignStart Program, which provides a low-cost avenue to market entry for companies developing custom SoCs around Cortex-M technology. For more information on Eta Compute, visit

For more on IAR Embedded Workbench for Arm, visit


About the Author

Brandon Lewis

Brandon Lewis, Editor-in-Chief of Embedded Computing Design, is responsible for guiding the property's content strategy, editorial direction, and engineering community engagement, which includes IoT Design, Automotive Embedded Systems, the Power Page, Industrial AI & Machine Learning, and other publications. As an experienced technical journalist, editor, and reporter with an aptitude for identifying key technologies, products, and market trends in the embedded technology sector, he enjoys covering topics that range from development kits and tools to cyber security and technology business models. Brandon received a BA in English Literature from Arizona State University, where he graduated cum laude. He can be reached by email at

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