While there exist many ways to deploy machine learning models on microcontrollers, it is non-trivial to choose the optimal combination of frameworks and targets for a given application. Thus, automating the end-to-end benchmarking flow is of high relevance nowadays. A tool called MLonMCU is proposed in this paper and demonstrated by benchmarking the state-of-the-art TinyML frameworks TFLite for Microcontrollers and TVM effortlessly with a large number of configurations in a low amount of time.
@article{arxiv.2306.08951,
title = {MLonMCU: TinyML Benchmarking with Fast Retargeting},
author = {Philipp van Kempen and Rafael Stahl and Daniel Mueller-Gritschneder and Ulf Schlichtmann},
journal= {arXiv preprint arXiv:2306.08951},
year = {2024}
}
Comments
CODAI 2022 Workshop - Embedded System Week (ESWeek)