ELSA: Exploiting Layer-wise N:M Sparsity for Vision Transformer Acceleration

TL;DR

This paper introduces ELSA, a method for customizing layer-wise N:M sparsity configurations in vision transformers to optimize inference speed and memory use on specialized accelerators, with minimal accuracy loss.

Contribution

ELSA is the first approach to optimize layer-wise N:M sparsity for ViTs, considering accelerator support and throughput, enabling significant FLOP reduction with negligible accuracy impact.

Findings

Achieves 2.9× FLOPs reduction on Swin-B and DeiT-B models.

Maintains high accuracy with minimal degradation on ImageNet.

Effectively leverages mixed sparsity support in accelerators.

Abstract

$N{:}M$ sparsity is an emerging model compression method supported by more and more accelerators to speed up sparse matrix multiplication in deep neural networks. Most existing $N{:}M$ sparsity methods compress neural networks with a uniform setting for all layers in a network or heuristically determine the layer-wise configuration by considering the number of parameters in each layer. However, very few methods have been designed for obtaining a layer-wise customized $N{:}M$ sparse configuration for vision transformers (ViTs), which usually consist of transformer blocks involving the same number of parameters. In this work, to address the challenge of selecting suitable sparse configuration for ViTs on $N{:}M$ sparsity-supporting accelerators, we propose ELSA, Exploiting Layer-wise $N{:}M$ Sparsity for ViTs. Considering not only all $N{:}M$ sparsity levels supported by a given accelerator but also the expected throughput improvement, our methodology can reap the benefits of accelerators supporting mixed sparsity by trading off negligible accuracy loss with both memory usage and inference time reduction for ViT models. For instance, our approach achieves a noteworthy 2.9$\times$ reduction in FLOPs for both Swin-B and DeiT-B with only a marginal degradation of accuracy on ImageNet. Our code will be released upon paper acceptance.