https://github.com/neu-spiral/CaP

https://github.com/neu-spiral/CaP

Last synced: 2 months ago
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• Host: GitHub
• URL: https://github.com/neu-spiral/CaP
• Owner: neu-spiral
• Created: 2022-03-17T00:08:33.000Z (over 2 years ago)
• Default Branch: master
• Last Pushed: 2023-01-10T21:22:08.000Z (over 1 year ago)
• Last Synced: 2024-01-29T00:19:10.724Z (5 months ago)
• Language: Python
• Size: 596 KB
• Stars: 3
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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• awesome-edge-computing - Communication-Aware DNN Pruning (CaP)

README

        
# 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:

```bash

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:

```bash

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}

}

```