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arXiv · 1911.08606

CoopNet: Cooperative Convolutional Neural Network for Low-Power MCUs

Luca Mocerino·Andrea Calimera
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Abstract

Fixed-point quantization and binarization are two reduction methods adopted to deploy Convolutional Neural Networks (CNN) on end-nodes powered by low-power micro-controller units (MCUs). While most of the existing works use them as stand-alone optimizations, this work aims at demonstrating there is margin for a joint cooperation that leads to inferential engines with lower latency and higher accuracy. Called CoopNet, the proposed heterogeneous model is conceived, implemented and tested on off-the-shelf MCUs with small on-chip memory and few computational resources. Experimental results conducted on three different CNNs using as test-bench the low-power RISC core of the Cortex-M family by ARM validate the CoopNet proposal by showing substantial improvements w.r.t. designs where quantization and binarization are applied separately.

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Luca Mocerino, Andrea Calimera. 2019-11-19. CoopNet: Cooperative Convolutional Neural Network for Low-Power MCUs. https://doi.org/10.1109/icecs46596.2019.8964993

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