Efficient Inference of CNNs via Channel Pruning
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The deployment of Convolutional Neural Networks (CNNs) on resource constrained platforms such as mobile devices and embedded systems has been greatly hindered by their high implementation cost, and thus motivated a lot research interest in compressing and accelerating trained CNN models. Among various techniques proposed in literature, structured pruning, especially channel pruning, has gain a lot focus due to 1) its superior performance in memory, computation, and energy reduction; and 2) it is friendly to existing hardware and software libraries. In this paper, we investigate the intermediate results of convolutional layers and present a novel pivoted QR factorization based channel pruning technique that can prune any specified number of input channels of any layer. We also explore more pruning opportunities in ResNet-like architectures by applying two tweaks to our technique. Experiment results on VGG-16 and ResNet-50 models with ImageNet ILSVRC 2012 dataset are very impressive with 4.29X and 2.84X computation reduction while only sacrificing about 1.40% top-5 accuracy. Compared to many prior works, the pruned models produced by our technique require up to 47.7% less computation while still achieve higher accuracies.
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