CoopNet: Cooperative Convolutional Neural Network for Low-Power MCUs

TL;DR

CoopNet introduces a cooperative approach combining quantization and binarization in CNNs, optimized for low-power MCUs, resulting in lower latency and higher accuracy compared to separate methods.

Contribution

This work demonstrates the effectiveness of jointly optimizing quantization and binarization in CNNs for low-power micro-controllers, improving performance over separate optimizations.

Findings

Substantial improvements in latency and accuracy over separate optimization methods.

Validated on three CNNs using ARM Cortex-M low-power cores.

Effective deployment on MCUs with limited memory and computational resources.

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.