Quantized CNN: A Unified Approach to Accelerate and Compress Convolutional Networks
文献类型：期刊论文
| 作者 | Cheng, Jian3,4,5 ; Wu, Jiaxiang2,3,4; Leng, Cong3,4; Wang, Yuhang1,3,4; Hu, Qinghao3,4
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| 刊名 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
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| 出版日期 | 2018-10-01 |
| 卷号 | 29期号:10页码:4730-4743 |
| 关键词 | Acceleration And Compression Convolutional Neural Network (Cnn) Mobile Devices Product Quantization |
| ISSN号 | 2162-237X |
| DOI | 10.1109/TNNLS.2017.2774288 |
| 文献子类 | Article |
| 英文摘要 | 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. |
| WOS关键词 | LEARNING BINARY-CODES ; ITERATIVE QUANTIZATION ; PROCRUSTEAN APPROACH ; RECOGNITION ; FPGAS |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000445351300015 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | 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 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/27920]  |
| 专题 | 类脑芯片与系统研究 |
| 通讯作者 | Cheng, Jian |
| 作者单位 | 1.UISEE Technol Beijing Ltd, Beijing 102402, Peoples R China 2.Tencent AI Lab, Machine Learning Grp, Shenzhen 518000, Peoples R China 3.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100190, Peoples R China 5.CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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