تقرير
U-TOE: Universal TinyML On-board Evaluation Toolkit for Low-Power IoT
| العنوان: | U-TOE: Universal TinyML On-board Evaluation Toolkit for Low-Power IoT |
|---|---|
| المؤلفون: | Huang, Zhaolan, Zandberg, Koen, Schleiser, Kaspar, Baccelli, Emmanuel |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Computer Science - Performance |
| الوصف: | 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. Comment: to be published in the proceedings of IFIP/IEEE PEMWN 2023 |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.23919/PEMWN58813.2023.10304946 |
| URL الوصول: | http://arxiv.org/abs/2306.14574 |
| رقم الأكسشن: | edsarx.2306.14574 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.23919/PEMWN58813.2023.10304946 |
|---|