- Abstract: The learning capability of a neural network improves with increasing depth at higher computational costs. Wider layers with dense kernel connectivity patterns further increase this cost and may hinder real-time inference. We propose feature map and kernel level pruning for reducing the computational complexity of a deep convolutional neural network. Pruning feature maps reduces the width of a layer and hence does not need any sparse representation. Further, kernel pruning changes the dense connectivity pattern into a sparse one. Due to coarse nature, these pruning granularities can be exploited by GPUs and VLSI based implementations. We propose a simple strategy to choose the least adversarial pruning masks. The proposed approach is generic and can select good pruning masks for feature map, kernel and intra-kernel pruning. The pruning masks are generated randomly, and the best performing one is selected using the evaluation set. The sufficient number of random pruning masks to try depends on the pruning ratio, and is around 100 when 40% complexity reduction is needed. The pruned network is retrained to compensate for the loss in accuracy. We have extensively evaluated the proposed approach with the CIFAR-10, SVHN and MNIST datasets. Experiments with the CIFAR-10 dataset show that more than 85% sparsity can be induced in the convolution layers with less than 1% increase in the misclassification rate of the baseline network.
- TL;DR: This work has proposed a new pruning strategy for CNN. Further, feature map and kernel pruning granularities are proposed for good pruning ratios and simple sparse representation.
- Conflicts: snu.ac.kr, dsp.snu.ac.kr