Eta Compute’s Tensai Flow Puts Machine Learning at the Edge of the IoT

August 11, 2020 Rich Nass

Deploying artificial intelligence and machine learning at the Edge of the IoT has long been the Holy Grail for design engineers. In most cases, there simply wasn’t enough compute power to tackle such complex operations, often with limited power resources available. Thanks for tools like Tensai Flow, the software suite in Eta Compute’s Tensai Platform, developers can now implement such systems.

Tensai Flow, which enables seamless design from concept to firmware, includes a compiler, a neural network zoo, and middleware with FreeRTOS, a hardware abstraction layer (HAL) and frameworks for sensors and IoT/cloud enablement.

The software suite complements the company’s existing resources to speed applications development. According to the company, the software addresses all aspects of designing and building a machine learning application for IoT and low power edge devices. This includes a reduced memory footprint, fewer operations, and overall less complexity.

A key feature of the Tensai software is its ability to reduce development risk by confirming feasibility and proof of concept. The neural network zoo accelerates and simplifies development with ready-to-use networks for the most common use cases, including motion, image, and sound classification; developers simply train the networks with their data.

One result is that TensorFlow networks can run on Eta Compute's ultra low power SoC. Testing has shown AI performance in the 1-mW range, which is quite low compared to other alternatives, particularly those designed for image processing.

About the Author

Rich Nass

Richard Nass is the Executive Vice-President of OpenSystems Media. His key responsibilities include setting the direction for all aspects of OpenSystems Media’s Embedded and IoT product portfolios, including web sites, e-newsletters, print and digital magazines, and various other digital and print activities. He was instrumental in developing the company's on-line educational portal, Embedded University. Previously, Nass was the Brand Director for UBM’s award-winning Design News property. Prior to that, he led the content team for UBM Canon’s Medical Devices Group, as well all custom properties and events in the U.S., Europe, and Asia. Nass has been in the engineering OEM industry for more than 25 years. In prior stints, he led the Content Team at EE Times, handling the Embedded and Custom groups and the TechOnline DesignLine network of design engineering web sites. Nass holds a BSEE degree from the New Jersey Institute of Technology.

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