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2024 | OriginalPaper | Chapter

Tiny Neural Deep Clustering: An Unsupervised Approach for Continual Machine Learning on the Edge

Authors : Giovanni Poletti, Andrea Albanese, Matteo Nardello, Davide Brunelli

Published in: Applications in Electronics Pervading Industry, Environment and Society

Publisher: Springer Nature Switzerland

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Abstract

Traditional tiny machine learning systems are widely employed because of their limited energy consumption, fast execution, and easy deployment. However, such systems have limited access to labelled data and need periodic maintenance due to the evolution of data distribution (i.e., context drift). Continual machine learning algorithms can enable continuous learning on embedded systems by updating their parameters, addressing context drift, and allowing neural networks to learn new categories over time. However, the availability of labelled data is scarce, limiting such algorithms in supervised settings. This paper overcomes this limitation with an alternative approach which combines supervised deep learning with unsupervised clustering to enable unsupervised continual machine learning on the edge. Tiny Neural Deep Clustering (TinyNDC) is deployed in an OpenMV Cam H7 Plus and tested with the MNIST dataset reaching a classification accuracy of 92.3% and a frame rate of 44 FPS.

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Footnotes
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Metadata
Title
Tiny Neural Deep Clustering: An Unsupervised Approach for Continual Machine Learning on the Edge
Authors
Giovanni Poletti
Andrea Albanese
Matteo Nardello
Davide Brunelli
Copyright Year
2024
Book
Applications in Electronics Pervading Industry, Environment and Society

Print ISBN: 978-3-031-48120-8

Electronic ISBN: 978-3-031-48121-5

Copyright Year: 2024

DOI
https://doi.org/10.1007/978-3-031-48121-5_17