# CoopNet: Cooperative Convolutional Neural Network for Low-Power MCUs

arXiv: https://arxiv.org/abs/1911.08606 · HTML: https://mlsanity.com/paper/1911.08606/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2019.

## Built on by (most cited first, 4 of 4)

- [Differentiable Network Pruning for Microcontrollers](https://mlsanity.com/paper/2110.08350/) (2021)
- [Pex: Memory-efficient Microcontroller Deep Learning through Partial Execution](https://mlsanity.com/paper/2211.17246/) (2022)
- [Fast Data Aware Neural Architecture Search via Supernet Accelerated Evaluation](https://mlsanity.com/paper/2502.12690/) (2025)
- [Big-Little Adaptive Neural Networks on Low-Power Near-Subthreshold Processors](https://mlsanity.com/paper/2304.09695/) (2023)

## Abstract (arXiv)

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.