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Pré-Publication, Document De Travail (Preprint/Prepublication) Année : 2024

RIOT-ML: Toolkit for Over-the-Air Secure Updates and Performance Evaluation of TinyML Models

Résumé

Practitioners in the field of TinyML lack so far a comprehensive, ”batteries-included” toolkit to streamline continuous integration, continuous deployment and performance assessments of executing diverse machine learning models on various low-power IoT hardware. Addressing this gap, our paper introduces RIOT-ML, a versatile toolkit crafted to assist IoT designers and researchers in these tasks. To this end, we designed RIOT-ML based on an integration of an array of functionalities from a low- power embedded OS, a universal model transpiler and compiler, a toolkit for TinyML performance measurement, and a low-power over-the-air secure update framework – all of which usable on an open-access IoT testbed available to the community. Our open source implementation of RIOT-ML and the initial experiments we report on showcase its utility in experimentally evaluating TinyML model performance across fleets of low-power IoT boards under test in the field, featuring a wide spectrum of heterogeneous microcontroller architectures and fleet network connectivity configurations. The existence of an open source toolkit such as RIOT-ML is essential to expedite research combining Artificial Intelligence and IoT, and to foster the full realization of edge computing’s potential.
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Dates et versions

hal-04506370 , version 1 (15-03-2024)

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  • HAL Id : hal-04506370 , version 1

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Zhaolan Huang, Koen Zandberg, Kaspar Schleiser, Emmanuel Baccelli. RIOT-ML: Toolkit for Over-the-Air Secure Updates and Performance Evaluation of TinyML Models. 2024. ⟨hal-04506370⟩
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