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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%).
<p align="center"> <img src="./intro.png" width="850" height="400"> </p>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}
}