This codebase contains the implementation of SparCL: Sparse Continual Learning on the Edge (NeurIPS2022).

In this work, we propose a novel framework called Sparse Continual Learning (SparCL), which is the first study that leverages sparsity to enable cost-effective continual learning on edge devices. SparCL achieves both training acceleration and accuracy preservation through the synergy of three aspects: weight sparsity, data efficiency, and gradient sparsity. SparCL outperforms competitive baselines obtained from adapting SOTA sparse training methods to the CL setting in both efficiency and accuracy. We also evaluate the effectiveness of SparCL on a real mobile phone, further indicating the practical potential of our method.

The code is developed using PyTorch>=1.2.0.

We provide a sample bash script to run our method at 0.75 sparsity ratio.

To run SparCL:

bash ./scripts/resnet18/run_mag_prune_irr_sp_EM_0.75_gradient0.80.sh

Note: In this version, we made some slight changes from the paper. For example, we observe that setting $\alpha=\beta$ in (1) and (3) results in similar performance. To simplify the logic, we merge the two importance scores w.r.t. current data and buffered data as a single term.

@inproceedings{
wang2022sparcl,
title={Spar{CL}: Sparse Continual Learning on the Edge},
author={Zifeng Wang and Zheng Zhan and Yifan Gong and Geng Yuan and Wei Niu and Tong Jian and Bin Ren and Stratis Ioannidis and Yanzhi Wang and Jennifer Dy},
booktitle={Advances in Neural Information Processing Systems},
editor={Alice H. Oh and Alekh Agarwal and Danielle Belgrave and Kyunghyun Cho},
year={2022},
url={https://openreview.net/forum?id=n0dD3d54Wgf}
}