Official Implementation of CaP @ INFOCOM 23

This codebase contains the implementation of [Communication-Aware DNN Pruning] (INFOCOM2023).

Introduction

We propose a Communication-aware Pruning (CaP) algorithm, a novel distributed inference framework for distributing DNN computations across a physical network. Departing from conventional pruning methods, CaP takes the physical network topology into consideration and produces DNNs that are communication-aware, designed for both accurate and fast execution over such a distributed deployment. Our experiments on CIFAR-10 and CIFAR-100, two deep learning benchmark datasets, show that CaP beats state of the art competitors by up to 4% w.r.t. accuracy on benchmarks. On experiments over real-world scenarios, it simultaneously reduces total execution time by 27%--68% at negligible performance decrease (less than 1%).

Environment Setup

Please install the python dependencies and packages found below:

pytorch-1.6.0
numpy-1.16.1
scipy-1.3.1
tqdm-4.33.0
yaml-0.1.7

Instructions

We provide a sample bash script to run our method at 0.75 sparsity ratio on CIFAR-10.

To run CaP:

source env.sh
run-cifar10-resnet18.sh

Cite

@article{jian2023cap,
  title={Communication-Aware DNN Pruning},
  author={Jian, Tong and Roy, Debashri Roy and Salehi, Batool and Soltani, Nasim and Chowdhury, Kaushik and Ioannidis, Stratis}
  journal={INFOCOM},
  year={2023}
}