MLonMCU: TinyML Benchmarking with Fast Retargeting
Philipp van Kempen Rafael Stahl Daniel Mueller-Gritschneder Ulf Schlichtmann
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.