Tiny machine learning (TinyML) in IoT systems exploits MCUs as edge devices for data processing. However, traditional TinyML methods can only perform inference, limited to static environments or classes. Real case scenarios usually work in dynamic environments, thus drifting the context where the original neural model is no more suitable. For this reason, pre-trained models reduce accuracy and reliability during their lifetime because the data recorded slowly becomes obsolete or new patterns appear. Continual learning strategies maintain the model up to date, with runtime fine-tuning of the parameters. This paper compares four state-of-the-art algorithms in two real applications: i) gesture recognition based on accelerometer data and ii) image classification. Our results confirm these systems' reliability and the feasibility of deploying them in tiny-memory MCUs, with a drop in the accuracy of a few percentage points with respect to the original models for unconstrained computing platforms.

Incremental Online Learning Algorithms Comparison for Gesture and Visual Smart Sensors / Avi, Alessandro; Albanese, Andrea; Brunelli, Davide. - (2022), pp. 1-8. ((Intervento presentato al convegno IJCNN 2022 tenutosi a Padua, Italy nel 18th-23th July 2022 [10.1109/IJCNN55064.2022.9892356].

Incremental Online Learning Algorithms Comparison for Gesture and Visual Smart Sensors

Albanese, Andrea
Secondo
;
Brunelli, Davide
Ultimo
2022-01-01

Abstract

Tiny machine learning (TinyML) in IoT systems exploits MCUs as edge devices for data processing. However, traditional TinyML methods can only perform inference, limited to static environments or classes. Real case scenarios usually work in dynamic environments, thus drifting the context where the original neural model is no more suitable. For this reason, pre-trained models reduce accuracy and reliability during their lifetime because the data recorded slowly becomes obsolete or new patterns appear. Continual learning strategies maintain the model up to date, with runtime fine-tuning of the parameters. This paper compares four state-of-the-art algorithms in two real applications: i) gesture recognition based on accelerometer data and ii) image classification. Our results confirm these systems' reliability and the feasibility of deploying them in tiny-memory MCUs, with a drop in the accuracy of a few percentage points with respect to the original models for unconstrained computing platforms.
2022 International Joint Conference on Neural Networks (IJCNN) Conference Proceedings
Piscataway, New Jersey, Stati Uniti
IEEE Institute of Electrical and Electronics Engineers
978-1-7281-8671-9
Avi, Alessandro; Albanese, Andrea; Brunelli, Davide
Incremental Online Learning Algorithms Comparison for Gesture and Visual Smart Sensors / Avi, Alessandro; Albanese, Andrea; Brunelli, Davide. - (2022), pp. 1-8. ((Intervento presentato al convegno IJCNN 2022 tenutosi a Padua, Italy nel 18th-23th July 2022 [10.1109/IJCNN55064.2022.9892356].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/355004
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