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IEEE COINS 2023 Contest for In Sensor Machine Learning Computing
  • +2
  • Danilo Pau ,
  • Andrey Korobitsyn ,
  • Dmitriy Proshin ,
  • Danil Zherebtsov ,
  • Marco Bianco
Danilo Pau
STMicroelectronics

Corresponding Author:[email protected]

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Andrey Korobitsyn
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Dmitriy Proshin
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Danil Zherebtsov
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Marco Bianco
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Abstract

There is a vast consensus in the embedded system community about the need to support artificial intelligence at the edge. An increasing demand for IoT and automotive devices is experienced with such a mandatory need to provide additional value to the user. Pushing further the need at its extreme, the device becomes a sensing element integrated in a ultra tiny package. To stimulate further progress in that respect a challenge has been set to the 2023 edition of the IEEE COINS conference. This paper describes this challenge as well as its results. It was aimed to let international teams use an end-to-end novel methodology to automatically conceive and deploy machine learning solutions for human activity recognition, as exemplary case study. The target tiny device was an intelligent sensor processor equipped with programmable capabilities to deploy neuton.ai automatically generated machine learning workloads. The contest winning team devised a tiny neural model which achieved balanced accuracy of 81.4% over 24 classes on neuton.ai deep learning framework. The model was deployed on the sensor, clocked at 10 MHz, and required a total memory of 19 KiB and its inference latency was 152 ms.