Speeding Up Deep Convolutional Neural Networks Based on Tucker-CP Decomposition

Abstract

Convolutional neural networks (CNNs) have made great success in computer vision tasks. But the computational complexity of CNNs is huge, which makes CNNs run slowly especially when computational resources are limited. In this paper, we propose a scheme based on tensor decomposition to accelerate CNNs. Firstly, Tucker method is used to decompose the convolution kernel into a small core tensor with key information and two factor matrices reflecting the linear relationship in the third dimension and fourth dimension of the convolution kernel respectively. Then CP (CANDECOMP/PARAFAC) method is used to decompose the core tensor into several rank-1 tensors. This scheme can remove the linear redundancy in convolution kernels and greatly speed up CNNs while maintaining the high classification accuracy. The scheme is used to decompose all the convolutional layers in AlexNet, and the accelerated model is trained and tested on ImageNet. The results show that our scheme achieves a whole-model speedup of 4 x with merely a 1.9% increase in top-5 error for AlexNet.