Pruning Very Deep Neural Network Channels for Efficient Inference

• URL: http://arxiv.org/abs/2211.08339v1
• Date: Mon, 14 Nov 2022 06:48:33 GMT
• Title: Pruning Very Deep Neural Network Channels for Efficient Inference
• Authors: Yihui He
• Abstract summary: Given a trained CNN model, we propose an iterative two-step algorithm to effectively prune each layer. VGG-16 achieves the state-of-the-art results by 5x speed-up along with only 0.3% increase of error. Our method is able to accelerate modern networks like ResNet, Xception and suffers only 1.4%, 1.0% accuracy loss under 2x speed-up respectively.
• Score: 6.497816402045099
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we introduce a new channel pruning method to accelerate very deep convolutional neural networks. Given a trained CNN model, we propose an iterative two-step algorithm to effectively prune each layer, by a LASSO regression based channel selection and least square reconstruction. We further generalize this algorithm to multi-layer and multi-branch cases. Our method reduces the accumulated error and enhances the compatibility with various architectures. Our pruned VGG-16 achieves the state-of-the-art results by 5x speed-up along with only 0.3% increase of error. More importantly, our method is able to accelerate modern networks like ResNet, Xception and suffers only 1.4%, 1.0% accuracy loss under 2x speed-up respectively, which is significant. Our code has been made publicly available.

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