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Communication Dans Un Congrès Année : 2023

Addressing limitations of TinyML approaches for AI-enabled Ambient Intelligence

Résumé

Integrating Artificial Intelligence (AI) into embedded systems is critical for the development of viable Ambient Intelligence (AmI). However, the energy requirements of current AI computations are not compatible with the limited resources of AmI devices and global sustainability goals. To address this issue, we propose using Federated Learning across multiple intermittent embedded systems to distribute data gathering, storage, and load balancing, thus enabling the system to assess and adapt appropriately to the environment. We initially focus on relevant real-world data acquisition to check the minimum data required by a local node in a federation. Our preliminary results demonstrate a direct correlation between signal decimation, a decrease in both training time and energy usage, and a collapsing threshold limit in accuracy.
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hal-04250026 , version 1 (19-10-2023)

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Paternité

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  • HAL Id : hal-04250026 , version 1

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Antoine Bonneau, Frédéric Le Mouël, Fabien Mieyeville. Addressing limitations of TinyML approaches for AI-enabled Ambient Intelligence. Workshop on Simplification, Compression, Efficiency and Frugality for Artificial intelligence (SCEFA), in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Politecnico di Torino; CENTAI, Sep 2023, Turin, Italy. ⟨hal-04250026⟩
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