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Communication Dans Un Congrès Année : 2023

U-TOE: Universal TinyML On-Board Evaluation Toolkit for Low-Power IoT

Résumé

Results from the TinyML community demonstrate that, it is possible to execute machine learning models directly on the terminals themselves, even if these are small microcontrollerbased devices. However, to date, practitioners in the domain lack convenient all-in-one toolkits to help them evaluate the feasibility of executing arbitrary models on arbitrary low-power IoT hardware. To this effect, we present in this paper U-TOE, a universal toolkit we designed to facilitate the task of IoT designers and researchers, by combining functionalities from a low-power embedded OS, a generic model transpiler and compiler, an integrated performance measurement module, and an open-access remote IoT testbed. We provide an open source implementation of U-TOE and we demonstrate its use to experimentally evaluate the performance of various models, on a wide variety of low-power IoT boards, based on popular microcontroller architectures. U-TOE allows easily reproducible and customizable comparative evaluation experiments on a wide variety of IoT hardware allat-once. The availability of a toolkit such as U-TOE is desirable to accelerate research combining Artificial Intelligence and IoT towards fully exploiting the potential of edge computing.
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Dates et versions

hal-04382496 , version 1 (11-01-2024)

Identifiants

Citer

Zhaolan Huang, Koen Zandberg, Kaspar Schleiser, Emmanuel Baccelli. U-TOE: Universal TinyML On-Board Evaluation Toolkit for Low-Power IoT. PEMWN 2023 - 12th IFIP/IEEE International Conference on Performance Evaluation and Modeling in Wired and Wireless Networks, Sep 2023, Berlin, Germany. pp.1-6, ⟨10.23919/PEMWN58813.2023.10304946⟩. ⟨hal-04382496⟩
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