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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