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Usability and Performance Analysis of Embedded Development Environment for On-device Learning
April 12, 2024, 4:42 a.m. | Enzo Scaffi (DYNAMID), Antoine Bonneau (DYNAMID, EE), Fr\'ed\'eric Le Mou\"el (DYNAMID), Fabien Mieyeville (INL, EE)
cs.LG updates on arXiv.org arxiv.org
Abstract: This research empirically examines embedded development tools viable for on-device TinyML implementation. The research evaluates various development tools with various abstraction levels on resource-constrained IoT devices, from basic hardware manipulation to deployment of minimalistic ML training. The analysis encompasses memory usage, energy consumption, and performance metrics during model training and inference and usability of the different solutions. Arduino Framework offers ease of implementation but with increased energy consumption compared to the native option, while RIOT …
abstract abstraction analysis arxiv basic consumption cs.ai cs.lg cs.se deployment development development tools devices embedded energy environment hardware implementation iot manipulation memory on-device learning performance performance analysis research tinyml tools training type usability usage
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