This work studies the composability of the building blocks of structural CNN models (e.g., GoogleLeNet and Residual Networks) in the context of network pruning. We empirically validate that a network composed of pre-trained building blocks (e.g. residual blocks and Inception modules) not only gives a better initial setting for training, but also allows the training process to converge at a significantly higher accuracy in much less time. Based on that insight, we propose a

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... design for CNN network pruning. Experiments show that this new scheme shortens the configuration process in CNN network pruning by up to 186.8X for ResNet-50 and up to 30.2X for Inception-V3, and meanwhile, the models it finds that meet the accuracy requirement are significantly more compact than those found by default schemes.