Cross-Channel Intragroup Sparsity Neural Network
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Modern deep neural network models generally build upon heavy over-parameterization for their exceptional performance. Network pruning is one often employed approach to obtain less demanding models for their deployment. Fine-grained pruning, while can achieve good model compression ratio, introduces irregularity in the computing data flow, often does not give improved model inference efficiency. Coarse-grained model pruning, while allows good inference speed through removing network weights in whole groups, for example, a whole filter, can lead to significant model performance deterioration. In this study, we introduce the cross-channel intragroup (CCI) sparsity structure that can avoid the inference inefficiency of fine-grained pruning while maintaining outstanding model performance.
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