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A RISC-V Extended Infrastructure for CNNs Through Pipelined Computing and Data Dependence Optimization

文献类型：期刊论文

作者Luo, Teng1,3; Xia, Tengfei1,3; Chen, Jiayuan2; Fan, Zhihua1,3; Li, Wenming1,3; Mu, Yudong1,3; An, Xuejun1,3; Ye, Xiaochun1,3; Fan, Dongrui1,3
刊名IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
出版日期2025-11-01
卷号44期号:11页码:4141-4154
关键词Artificial intelligence Convolution Convolutional neural networks Computer architecture Computational efficiency Pipelines Logic Filters Fans Biological system modeling Convolutional neural networks (CNNs) acceleration dataflow optimization pipelined computing RISC-V extended instructions
ISSN号0278-0070
DOI10.1109/TCAD.2025.3565470
英文摘要With the rapid development of Artificial Intelligence (AI), convolutional neural networks (CNNs) have been widely applied in fields like computer vision and recommendation systems. This growth has intensified the demand for hardware acceleration of CNNs. Existing accelerators are either designed as co-processors or improve performance through extended instructions. While these methods can significantly improve performance, they often result in limited programming and execution flexibility. In this article, we design custom RISC-V instructions specifically for CNNs to maximize data reuse and exploit parallelism. Then, to efficiently execute CNNs instructions, we extend a pipelined vector computing unit (PPVCU). Finally, we incorporate pattern detection logic (PDL) to identify common data dependence patterns in CNNs, enabling the data dependence computing unit (DDCU) to process instructions within each pattern in parallel. Experimental results show that our approach achieves, on average, 9.54x performance improvement and 6.7x energy efficiency improvement compared to our baseline, 8.34x performance improvement, and 3.1x energy efficiency improvement compared to state-of-the-art designs.
资助项目National Key Research and Development Program of China[2023YFB4503500] ; Institute of Computing Technology, Chinese Academy of Sciences a AT China Mobile Communications Group Company, Ltd. ; Joint Institute, Beijing Nova Program[20220484054] ; Joint Institute, Beijing Nova Program[20230484420] ; Beijing Natural Science Foundation[L234078] ; SKLP Foundation[CLQD202502]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:001600047600021
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/41610]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Chen, Jiayuan; Fan, Zhihua
作者单位1.Chinese Acad Sci, Inst Comp Technol, SKLP, Beijing 100190, Peoples R China
2.China Mobile Res Inst, Res Dept Network & IT Technol, Beijing 100032, Peoples R China
3.Univ Chinese Acad Sci, Sch Comp Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Luo, Teng,Xia, Tengfei,Chen, Jiayuan,et al. A RISC-V Extended Infrastructure for CNNs Through Pipelined Computing and Data Dependence Optimization[J]. IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS,2025,44(11):4141-4154.
APA Luo, Teng.,Xia, Tengfei.,Chen, Jiayuan.,Fan, Zhihua.,Li, Wenming.,...&Fan, Dongrui.(2025).A RISC-V Extended Infrastructure for CNNs Through Pipelined Computing and Data Dependence Optimization.IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS,44(11),4141-4154.
MLA Luo, Teng,et al."A RISC-V Extended Infrastructure for CNNs Through Pipelined Computing and Data Dependence Optimization".IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 44.11(2025):4141-4154.

入库方式： OAI收割

来源：计算技术研究所

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