Knowledge Commons of Institute of Automation,CAS
Quantized CNN: A Unified Approach to Accelerate and Compress Convolutional Networks | |
Cheng, Jian1,2,3; Wu, Jiaxiang1,2,4; Leng, Cong1,2; Wang, Yuhang1,2,5; Hu, Qinghao1,2 | |
发表期刊 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS |
ISSN | 2162-237X |
2018-10-01 | |
卷号 | 29期号:10页码:4730-4743 |
文章类型 | Article |
摘要 | We are witnessing an explosive development and widespread application of deep neural networks (DNNs) in various fields. However, DNN models, especially a convolutional neural network (CNN), usually involve massive parameters and are computationally expensive, making them extremely dependent on high-performance hardware. This prohibits their further extensions, e.g., applications on mobile devices. In this paper, we present a quantized CNN, a unified approach to accelerate and compress convolutional networks. Guided by minimizing the approximation error of individual layer's response, both fully connected and convolutional layers are carefully quantized. The inference computation can be effectively carried out on the quantized network, with much lower memory and storage consumption. Quantitative evaluation on two publicly available benchmarks demonstrates the promising performance of our approach: with comparable classification accuracy, it achieves 4 to 6x acceleration and 15 to 20x compression. With our method, accurate image classification can even be directly carried out on mobile devices within 1 s. |
关键词 | Acceleration And Compression Convolutional Neural Network (Cnn) Mobile Devices Product Quantization |
WOS标题词 | Science & Technology ; Technology |
DOI | 10.1109/TNNLS.2017.2774288 |
关键词[WOS] | LEARNING BINARY-CODES ; ITERATIVE QUANTIZATION ; PROCRUSTEAN APPROACH ; RECOGNITION ; FPGAS |
收录类别 | SCI |
语种 | 英语 |
项目资助者 | National Natural Science Foundation of China(61332016) ; Scientific Research Key Program of Beijing Municipal Commission of Education(KZ201610005012) ; Fund of Hubei Key Laboratory of Transportation Internet of Things ; Fund of Jiangsu Key Laboratory of Big Data Analysis Technology |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000445351300015 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/27921 |
专题 | 复杂系统认知与决策实验室_高效智能计算与学习 |
通讯作者 | Cheng, Jian |
作者单位 | 1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100190, Peoples R China 3.CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China 4.Tencent AI Lab, Machine Learning Grp, Shenzhen 518000, Peoples R China 5.UISEE Technol Beijing Ltd, Beijing 102402, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Cheng, Jian,Wu, Jiaxiang,Leng, Cong,et al. Quantized CNN: A Unified Approach to Accelerate and Compress Convolutional Networks[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2018,29(10):4730-4743. |
APA | Cheng, Jian,Wu, Jiaxiang,Leng, Cong,Wang, Yuhang,&Hu, Qinghao.(2018).Quantized CNN: A Unified Approach to Accelerate and Compress Convolutional Networks.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,29(10),4730-4743. |
MLA | Cheng, Jian,et al."Quantized CNN: A Unified Approach to Accelerate and Compress Convolutional Networks".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 29.10(2018):4730-4743. |
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