Hardware-aware mobile building block evaluation for computer vision [article]

Maxim Bonnaerens, Matthias Freiberger, Marian Verhelst, Joni Dambre
2022 arXiv   pre-print
In this work we propose a methodology to accurately evaluate and compare the performance of efficient neural network building blocks for computer vision in a hardware-aware manner. Our comparison uses pareto fronts based on randomly sampled networks from a design space to capture the underlying accuracy/complexity trade-offs. We show that our approach allows to match the information obtained by previous comparison paradigms, but provides more insights in the relationship between hardware cost
more » ... d accuracy. We use our methodology to analyze different building blocks and evaluate their performance on a range of embedded hardware platforms. This highlights the importance of benchmarking building blocks as a preselection step in the design process of a neural network. We show that choosing the right building block can speed up inference by up to a factor of 2x on specific hardware ML accelerators.
arXiv:2208.12694v1 fatcat:gitqi2qkxnhndhsh3kvllzuxqq