Pruning Very Deep Neural Network Channels for Efficient Inference

11/14/2022

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by   Yihui He, et al.

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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 is able to accelerate modern networks like ResNet, Xception and suffers only 1.4 Our code has been made publicly available.