tinyML Talks: Enabling on-device learning on STM32 microcontrollers

“Enabling on-device learning on STM32 microcontrollers”

Beatrice Rossi
Research Scientist
STMicroelectronics

Michele Craighero
PhD Student
Politecnico di Milano

Most of today’s TinyML solutions carry out the inference at the edge, where data are acquired, while they entirely demand training to external resources. However, empowering edge devices with the ability to learn from local data has very important implications in terms of prediction and privacy as it would allow adapting a pretrained model to specific users or time-changing conditions without sharing data externally.
In this work, we present a framework in C programming language to train CNNs on STM32 microcontrollers. We adopt our framework to successfully personalize a 1D-CNN for Human Activity Recognition and we provide a software tool to estimate the memory and computational resources required to accomplish model personalization.

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