Tinyml benchmark: Executing fully connected neural networks on commodity microcontrollers
B Sudharsan, S Salerno, DD Nguyen… - 2021 IEEE 7th World …, 2021 - ieeexplore.ieee.org
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 …
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 …