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

• URL: http://arxiv.org/abs/2306.14574v2
• Date: Tue, 22 Aug 2023 20:38:47 GMT
• Title: U-TOE: Universal TinyML On-board Evaluation Toolkit for Low-Power IoT
• Authors: Zhaolan Huang, Koen Zandberg, Kaspar Schleiser and Emmanuel Baccelli
• Abstract summary: U-TOE is a universal toolkit designed to facilitate the task of IoT designers and researchers. We provide an open source implementation of U-TOE and demonstrate its use to experimentally evaluate the performance of various models.
• Score: 3.981958767941474
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 microcontroller-based 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 all-at-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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