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.

Tiny Neural Deep Clustering: An Unsupervised Approach for Continual Machine Learning on the Edge / Poletti, G.; Albanese, A.; Nardello, M.; Brunelli, D.. - 1110:(2024), pp. 117-123. (Intervento presentato al convegno APPLEPIES 2023 tenutosi a Genova nel 28th–29th September 2023) [10.1007/978-3-031-48121-5_17].

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

Albanese A.;Nardello M.;Brunelli D.
2024-01-01

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.
2024
Applications in Electronics Pervading Industry, Environment and Society APPLEPIES 2023
Cham, CH
Springer Science and Business Media Deutschland GmbH
978-3-031-48120-8
978-3-031-48121-5
Poletti, G.; Albanese, A.; Nardello, M.; Brunelli, D.
Tiny Neural Deep Clustering: An Unsupervised Approach for Continual Machine Learning on the Edge / Poletti, G.; Albanese, A.; Nardello, M.; Brunelli, D.. - 1110:(2024), pp. 117-123. (Intervento presentato al convegno APPLEPIES 2023 tenutosi a Genova nel 28th–29th September 2023) [10.1007/978-3-031-48121-5_17].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/402609
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