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arXiv · 2203.01077

Addressing Gap between Training Data and Deployed Environment by On-Device Learning

Kazuki Sunaga·Masaaki Kondo·Hiroki Matsutani
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Abstract

The accuracy of tinyML applications is often affected by various environmental factors, such as noises, location/calibration of sensors, and time-related changes. This article introduces a neural network based on-device learning (ODL) approach to address this issue by retraining in deployed environments. Our approach relies on semi-supervised sequential training of multiple neural networks tailored for low-end edge devices. This article introduces its algorithm and implementation on wireless sensor nodes consisting of a Raspberry Pi Pico and low-power wireless module. Experiments using vibration patterns of rotating machines demonstrate that retraining by ODL improves anomaly detection accuracy compared with a prediction-only deep neural network in a noisy environment. The results also show that the ODL approach can save communication cost and energy consumption for battery-powered Internet of Things devices.

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Kazuki Sunaga, Masaaki Kondo, Hiroki Matsutani. 2022-03-02. Addressing Gap between Training Data and Deployed Environment by On-Device Learning. https://doi.org/10.1109/mm.2023.3314711

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