Accelerating Convolutional Neural Networks for Mobile Applications
Wang, Peisong1,2; Cheng, Jian1,2,3
2016
会议名称ACM International Conference on Multimedia
页码541-545
会议日期2016.10-15-10.19
会议地点Amsterdam, Netherlands
摘要

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.


关键词Convolutional Neural Networks Acceleration Image Classification Tensor Decomposition
收录类别EI
语种英语
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/20148
专题模式识别国家重点实验室_图像与视频分析
作者单位1.Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
3.Center for Excellence in Brain Science and Intelligence Technology
推荐引用方式
GB/T 7714
Wang, Peisong,Cheng, Jian. Accelerating Convolutional Neural Networks for Mobile Applications[C],2016:541-545.
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