CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
Accelerating Convolutional Neural Networks for Mobile Applications
Wang, Peisong1,2; Cheng, Jian1,2,3
Conference NameACM International Conference on Multimedia
Conference Date2016.10-15-10.19
Conference PlaceAmsterdam, Netherlands

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.

KeywordConvolutional Neural Networks Acceleration Image Classification Tensor Decomposition
Indexed ByEI
Document Type会议论文
Affiliation1.Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
3.Center for Excellence in Brain Science and Intelligence Technology
Recommended Citation
GB/T 7714
Wang, Peisong,Cheng, Jian. Accelerating Convolutional Neural Networks for Mobile Applications[C],2016:541-545.
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