TinyML benchmark: Executing fully connected neural networks on commodity microcontrollers
Breslin, John G.
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Sudharsan, Bharath, Salerno, Simone, Nguyen, Duc-Duy, Yahya, Muhammad, Wahid, Abdul, Yadav, Piyush, & Breslin, John G. (2021). TinyML benchmark: Executing fully connected neural networks on commodity microcontrollers. Paper presented at the IEEE 7th World Forum on Internet of Things (WF-IoT 2021), New Orleans, Louisiana, USA, 20-24 June, DOI: 10.13025/rmkq-1966
Recent advancements in the field of ultra-low-power machine learning (TinyML) promises to unlock an entirely new class of edge applications. However, continued progress is restrained by the lack of benchmarking Machine Learning (ML) models on TinyML hardware, which is fundamental to this field reaching maturity. In this paper, we designed 3 types of fully connected Neural Networks (NNs), trained each NN using 10 datasets (produces 30 NNs), and present the benchmark by reporting the onboard model performance on 7 popular MCUboards (similar boards are used to design TinyML hardware). We open-sourced and made the complete benchmark results freely available online 1 to enable the TinyML community researchers and developers to systematically compare, evaluate, and improve various asp