中文版 | English
Title

A High Performance Multi-Bit-Width Booth Vector Systolic Accelerator for NAS Optimized Deep Learning Neural Networks

Author
Publication Years
2022-09
DOI
Source Title
ISSN
1558-0806
Volume69Issue:9Pages:3619-3631
Keywords
URL[Source Record]
Indexed By
SCI ; EI
Language
English
SUSTech Authorship
Others
EI Accession Number
20222612280659
EI Keywords
Benchmarking ; Computation theory ; Computer architecture ; Computer hardware ; Convolution ; Data handling ; Deep neural networks ; Energy efficiency ; Network architecture ; Systolic arrays
ESI Classification Code
Ergonomics and Human Factors Engineering:461.4 ; Energy Conservation:525.2 ; Information Theory and Signal Processing:716.1 ; Computer Theory, Includes Formal Logic, Automata Theory, Switching Theory, Programming Theory:721.1 ; Logic Elements:721.2 ; Computer Systems and Equipment:722 ; Data Processing and Image Processing:723.2
Data Source
IEEE
PDF urlhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9793397
Citation statistics
Cited Times [WOS]:1
Document TypeJournal Article
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/347863
DepartmentSUSTech Institute of Microelectronics
Affiliation
1.Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
2.School of Microelectronics, Southern University of Science and Technology, Shenzhen, China
Recommended Citation
GB/T 7714
Mingqiang Huang,Yucen Liu,Changhai Man,et al. A High Performance Multi-Bit-Width Booth Vector Systolic Accelerator for NAS Optimized Deep Learning Neural Networks[J]. IEEE Transactions on Circuits and Systems I: Regular Papers,2022,69(9):3619-3631.
APA
Mingqiang Huang.,Yucen Liu.,Changhai Man.,Kai Li.,Quan Cheng.,...&Hao Yu.(2022).A High Performance Multi-Bit-Width Booth Vector Systolic Accelerator for NAS Optimized Deep Learning Neural Networks.IEEE Transactions on Circuits and Systems I: Regular Papers,69(9),3619-3631.
MLA
Mingqiang Huang,et al."A High Performance Multi-Bit-Width Booth Vector Systolic Accelerator for NAS Optimized Deep Learning Neural Networks".IEEE Transactions on Circuits and Systems I: Regular Papers 69.9(2022):3619-3631.
Files in This Item:
File Name/Size DocType Version Access License
J106.A_High_Performa(4433KB)Journal Article作者接受稿Restricted AccessCC BY-NC-SA
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