CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
Recent advances in efficient computation of deep convolutional neural networks
Cheng, Jian1,2; Wang, Pei-song1,2; Li, Gang1,2; Hu, Qing-hao1,2; Lu, Han-qing1,2
Source PublicationFRONTIERS OF INFORMATION TECHNOLOGY & ELECTRONIC ENGINEERING
2018
Volume19Issue:1Pages:64-77
SubtypeReview
AbstractDeep neural networks have evolved remarkably over the past few years and they are currently the fundamental tools of many intelligent systems. At the same time, the computational complexity and resource consumption of these networks continue to increase. This poses a significant challenge to the deployment of such networks, especially in real-time applications or on resource-limited devices. Thus, network acceleration has become a hot topic within the deep learning community. As for hardware implementation of deep neural networks, a batch of accelerators based on a field-programmable gate array (FPGA) or an application-specific integrated circuit (ASIC) have been proposed in recent years. In this paper, we provide a comprehensive survey of recent advances in network acceleration, compression, and accelerator design from both algorithm and hardware points of view. Specifically, we provide a thorough analysis of each of the following topics: network pruning, low-rank approximation, network quantization, teacher-student networks, compact network design, and hardware accelerators. Finally, we introduce and discuss a few possible future directions.
KeywordDeep Neural Networks Acceleration Compression Hardware Accelerator
WOS HeadingsScience & Technology ; Technology
DOI10.1631/FITEE.1700789
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Information Systems ; Computer Science, Software Engineering ; Engineering, Electrical & Electronic
WOS IDWOS:000427742400007
Citation statistics
Cited Times:10[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20897
Collection模式识别国家重点实验室_图像与视频分析
Corresponding AuthorCheng, Jian
Affiliation1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Cheng, Jian,Wang, Pei-song,Li, Gang,et al. Recent advances in efficient computation of deep convolutional neural networks[J]. FRONTIERS OF INFORMATION TECHNOLOGY & ELECTRONIC ENGINEERING,2018,19(1):64-77.
APA Cheng, Jian,Wang, Pei-song,Li, Gang,Hu, Qing-hao,&Lu, Han-qing.(2018).Recent advances in efficient computation of deep convolutional neural networks.FRONTIERS OF INFORMATION TECHNOLOGY & ELECTRONIC ENGINEERING,19(1),64-77.
MLA Cheng, Jian,et al."Recent advances in efficient computation of deep convolutional neural networks".FRONTIERS OF INFORMATION TECHNOLOGY & ELECTRONIC ENGINEERING 19.1(2018):64-77.
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