Accelerating Convolutional Neural Networks for Mobile Applications
Convolutional neural networks (CNNs) have achieved remarkable performance in a wide range of computer vision tasks, typically at the cost of massive computational complexity. The low speed of these networks may hinder real time applications especially when computational resources are limited. In this paper, an efficient and effective approach is proposed to accelerate the test phase computation of CNNs based on low rank and group sparse tensor decomposition. Specifically, for each convolutional layer, the kernel tensor is decomposed into the sum of a small number of low multilinear rank tensors. Then we replace the original kernel tensors in all layers with the approximate tensors and fine tune the whole net with respect to the final classification task using standard backpropagation. Comprehensive experiments on ILSVRC 12 demonstrate significant reduction in computational complexity, at the cost of negligible loss in accuracy. For the widely used VGG 16 model, our approach obtains a 6.6$\times$ speed up on PC and 5.91$\times$ speed up on mobile device of the whole network with less than 1\ increase on top 5 error.
ACM Multimedia