Fixed-point Factorized Networks
Wang, Peisong1,2; Cheng, Jian1,2,3
2017
会议名称IEEE Conference on Computer Vision and Pattern Recognition
页码4012-4020
会议日期2017.7.21-7.26
会议地点Hawaii,USA
摘要

In recent years, Deep Neural Networks (DNN) based methods have achieved remarkable performance in a wide range of tasks and have been among the most powerful and widely used techniques in computer vision. However, DNN-based methods are both computational-intensive and resource-consuming, which hinders the application of these methods on embedded systems like smart phones. To alleviate this problem, we introduce a novel Fixed-point Factorized Networks (FFN) for pretrained models to reduce the computational complexity as well as the storage requirement of networks. The resulting networks have only weights of -1, 0 and 1, which significantly eliminates the most resource-consuming multiply-accumulate operations (MACs). Extensive experiments on large-scale ImageNet classification task show the proposed FFN only requires one-thousandth of multiply operations with comparable accuracy.


关键词Convolutional Neural Networks Ternary Quantization Network Acceleration Network Compression
收录类别EI
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/20114
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. Fixed-point Factorized Networks[C],2017:4012-4020.
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