tinyML On Device Learning – Manuel Roveri: Is on-device learning the next “big thing” in TinyML?
Is on-device learning the next “big thing” in TinyML?
Manuel ROVERI, Associate Professor, Politecnico di Milano
On-device tiny machine learning represents one of the most challenging and relevant research directions in Tiny Machine Learning (TinyML) with a strong impact from both the theoretical and the technological perspective. Indeed, On-device tiny machine learning will allow the design of smart objects and devices that will be able to learn TinyML models during the operational life, hence being able to adapt to evolving data-generating processes (e.g., due to periodicity or seasonality effect, faults or malfunctioning affecting sensors or actuators, or changes in the users’ behavior), a common situation in real-world application scenarios.
The aim of this talk is to explore on-device TinyML and introduce an on-device TinyML learning algorithm to support the incremental learning of TinyML models and their adaptation in presence of evolving data-generating processes directly on-device. Experimental results on two application scenarios and two off-the-shelf hardware platforms show the feasibility and effectiveness of the proposed solution.
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