题名 | Customized instruction on RISC-V for Winograd-based convolution acceleration |
作者 | |
通讯作者 | Ye,Terry Tao |
DOI | |
发表日期 | 2021-07-01
|
会议名称 | 2021 IEEE 32nd International Conference on Application-specific Systems, Architectures and Processors (ASAP)
|
ISSN | 1063-6862
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ISBN | 978-1-6654-2702-9
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会议录名称 | |
卷号 | 2021-text
|
页码 | 65-68
|
会议日期 | 7-9 July 2021
|
会议地点 | NJ, USA
|
摘要 | Convolution operation accounts for the major work-load in convolutional neural networks (CNN). However, standard instruction set for RISC-V processor cannot efficiently perform the matrix convolution between kernel and input matrices. In this paper, we construct a custom instruction under the RISC-V ISA that can perform the F(2×2, 3×3) convolution within one single execution. Particularly, optimized by the Winograd algorithm, the operation only needs 16 multiplications instead of 36 multiplications as needed by standard ISA. Benefit from this cycles, as compared to 140 cycles using standard instructions. Thenew instruction, F(2×2, 3×3) can be calculated within 19 clock power consumed during convolution operation is also reduced significantly. |
关键词 | |
学校署名 | 第一
; 通讯
|
语种 | 英语
|
相关链接 | [Scopus记录] |
收录类别 | |
WOS记录号 | WOS:000698747200011
|
EI入藏号 | 20213810904987
|
EI主题词 | Convolutional neural networks
; Network architecture
|
EI分类号 | Information Theory and Signal Processing:716.1
|
Scopus记录号 | 2-s2.0-85114932299
|
来源库 | Scopus
|
全文链接 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9516614 |
引用统计 |
被引频次[WOS]:4
|
成果类型 | 会议论文 |
条目标识符 | http://sustech.caswiz.com/handle/2SGJ60CL/245953 |
专题 | 工学院_电子与电气工程系 |
作者单位 | 1.Southern University of Science and Technology,Department of Electrical and Electronic Engineering,Shenzhen,China 2.University Key Laboratory of Advanced Wireless Communications of Guangdong Province,Southern University of Science and Technology,Shenzhen,China |
第一作者单位 | 电子与电气工程系 |
通讯作者单位 | 电子与电气工程系; 南方科技大学 |
第一作者的第一单位 | 电子与电气工程系 |
推荐引用方式 GB/T 7714 |
Wang,Shihang,Zhu,Jianghan,Wang,Qi,et al. Customized instruction on RISC-V for Winograd-based convolution acceleration[C],2021:65-68.
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条目包含的文件 | 条目无相关文件。 |
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