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An Efficient Deep Learning Accelerator Architecture for Compressed Video Analysis

文献类型：期刊论文

作者Wang, Yongchen1,2; Wang, Ying1,2; Li, Huawei1,2; Li, Xiaowei1,2
刊名IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
出版日期2022-09-01
卷号41期号:9页码:2808-2820
关键词Streaming media Neural networks Image coding Decoding Metadata Deep learning Hardware Neural network acceleration specialized accelerator video analysis
ISSN号0278-0070
DOI10.1109/TCAD.2021.3120076
英文摘要Previous neural network accelerators tailored to video analysis only accept data of RGB/YUV domain, requiring decompressing the video that are often compressed before transmitted from the edge sensors. A compressed video processing accelerator can alleviate the decoding overhead, and gain performance speedup by operating on more compact input data. This work proposes a novel deep learning accelerator architecture, Alchemist, which is able to predict results directly from the compressed video bitstream instead of reconstructing the full RGB images. By utilizing the metadata of motion vector and critical blocks extracted from bitstreams, Alchemist contributes to a remarkable performance speedup of 5x with negligible accuracy loss. Nevertheless, we still find that the original compressed video coded by standard algorithms such as H.264 is not suitable to be directly manipulated, due to diverse compressed structures. Although obviating the requirement to recover all RGB frames, the accelerator must parse the entire compressed video bitstream to locate reference frames and extract useful metadata. If we combine the video codec with the proposed compressed video analysis, additional optimizations can be obtained. Therefore, to cope with the mismatch between current video coding algorithms, such as H.264 and neural network-based video analysis, we propose a specialized coding strategy to generate compressed video bitstreams more suitable for transmission and analysis, which further simplifies the decoding stage of video analysis and is capable of achieving significant storage reduction.
资助项目National Key Research and Development Program of China[2020YFB1600201] ; National Natural Science Foundation of China (NSFC)[61874124] ; National Natural Science Foundation of China (NSFC)[2090024] ; National Natural Science Foundation of China (NSFC)[61876173]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000842062100007
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/19472]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Wang, Ying
作者单位1.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Yongchen,Wang, Ying,Li, Huawei,et al. An Efficient Deep Learning Accelerator Architecture for Compressed Video Analysis[J]. IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS,2022,41(9):2808-2820.
APA Wang, Yongchen,Wang, Ying,Li, Huawei,&Li, Xiaowei.(2022).An Efficient Deep Learning Accelerator Architecture for Compressed Video Analysis.IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS,41(9),2808-2820.
MLA Wang, Yongchen,et al."An Efficient Deep Learning Accelerator Architecture for Compressed Video Analysis".IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 41.9(2022):2808-2820.

入库方式： OAI收割

来源：计算技术研究所

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