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arXiv · 2210.00181

EAPruning: Evolutionary Pruning for Vision Transformers and CNNs

Qingyuan Li·Bo Zhang·Xiangxiang Chu
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Abstract

Structured pruning greatly eases the deployment of large neural networks in resource-constrained environments. However, current methods either involve strong domain expertise, require extra hyperparameter tuning, or are restricted only to a specific type of network, which prevents pervasive industrial applications. In this paper, we undertake a simple and effective approach that can be easily applied to both vision transformers and convolutional neural networks. Specifically, we consider pruning as an evolution process of sub-network structures that inherit weights through reconstruction techniques. We achieve a 50% FLOPS reduction for ResNet50 and MobileNetV1, leading to 1.37x and 1.34x speedup respectively. For DeiT-Base, we reach nearly 40% FLOPs reduction and 1.4x speedup. Our code will be made available.

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Qingyuan Li, Bo Zhang, Xiangxiang Chu. 2022-10-01. EAPruning: Evolutionary Pruning for Vision Transformers and CNNs. https://arxiv.org/abs/2210.00181

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