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An Edge 3D CNN Accelerator for Low-Power Activity Recognition

文献类型：期刊论文

作者Wang, Ying4,5; Wang, Yongchen4,5; Shi, Cong3; Cheng, Long2; Li, Huawei1,4,5; Li, Xiaowei4,5
刊名IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
出版日期2021-05-01
卷号40期号:5页码:918-930
关键词Three-dimensional displays Two dimensional displays Arrays Feature extraction System-on-chip Redundancy 3D CNN activity analysis CNN accelerator network-on-chip video
ISSN号0278-0070
DOI10.1109/TCAD.2020.3011042
英文摘要3D convolutional neural networks (CNNs) are gaining increasing popularity in the area of video-based action/activity analysis. Compared to 2D convolutions that share the filters in a 2D spatial domain, 3D convolutions further reuse filters in the temporal dimension to capture temporal-domain features in the video. How to exploit the data locality in the temporal dimension directly impacts the energy efficiency of specialized architectures for 3D CNN inference. Prior works on specialized 3D-CNN accelerators employ additional on-chip memories and multicluster architecture to reuse data among the process element (PE) arrays, which is very expensive for low-power chip implementation. Instead of harvesting in-memory data locality, we propose the architecture of systolic cube to exploit the spatial and temporal localities in 3D CNNs, which moves the reusable data in-between PEs connected via a 3D-cube network-on-chip. Furthermore, due to the existence of visual feature reappearance in the temporal domain, there exists a considerable portion of repetitive pixels and activations among the feature maps captured at adjacent time slots. To eliminate such temporal redundancy in 3D CNNs, the proposed accelerator architecture is equipped with a redundancy detection and elimination mechanism, capable of skipping the computations with the same activations and parameters when reusing the convolutional filters along the temporal dimension. In our evaluation, the experimental results show that the systolic-cube architecture contributes to a considerable energy-efficiency boost for state-of-the-art activity-recognition benchmarks and datasets.
资助项目National Natural Science Foundation of China[61874124] ; National Natural Science Foundation of China[61876173] ; Youth Innovation Promotion Association, CAS[2018138] ; National Key Research and Development Program of China[2018AAA0102700]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000641964100009
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/17791]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Li, Huawei; Li, Xiaowei
作者单位1.Peng Cheng Lab, Shenzhen, Peoples R China
2.Dublin City Univ, Insight Ctr Data Analyt, Sch Comp, Dublin D09 FW22 9, Ireland
3.Chongqing Univ, Sch Microelect & Commun Engn, Chongqing 400044, Peoples R China
4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
5.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Ying,Wang, Yongchen,Shi, Cong,et al. An Edge 3D CNN Accelerator for Low-Power Activity Recognition[J]. IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS,2021,40(5):918-930.
APA Wang, Ying,Wang, Yongchen,Shi, Cong,Cheng, Long,Li, Huawei,&Li, Xiaowei.(2021).An Edge 3D CNN Accelerator for Low-Power Activity Recognition.IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS,40(5),918-930.
MLA Wang, Ying,et al."An Edge 3D CNN Accelerator for Low-Power Activity Recognition".IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 40.5(2021):918-930.

入库方式： OAI收割

来源：计算技术研究所

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