HAS-RL: A Hierarchical Approximate Scheme Optimized With Reinforcement Learning for NoC-Based NN Accelerators-Reference-Cited by-同舟云学术

HAS-RL: A Hierarchical Approximate Scheme Optimized With Reinforcement Learning for NoC-Based NN Accelerators

Published:2024-04 Issue:4 Volume:71 Page:1863-1875
ISSN:1549-8328
Container-title:IEEE Transactions on Circuits and Systems I: Regular Papers
language:
Short-container-title:IEEE Trans. Circuits Syst. I

Author:

Li Siyue¹^ORCID,Zhou Shize¹,Xue Yongqi¹^ORCID,Fan Wenjie¹^ORCID,Cheng Tong¹^ORCID,Ji Jinlun¹^ORCID,Dai Chenyang¹,Song Wenqing¹^ORCID,Chen Qinyu²^ORCID,Gao Chang³,Li Li¹^ORCID,Fu Yuxiang¹^ORCID

Affiliation:

1. School of Integrated Circuits and School of Electronic Science and Engineering, Nanjing University, Qixia, Nanjing, Jiangsu, China

2. Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland

3. Electronic Circuits and Architectures (ELCA) Group, Faculty of Electrical Engineering, Mathematics, and Computer Science (EEMCS), Delft University of Technology, Delft, The Netherlands

Funder

National Natural Science Foundation of China

Joint Funds of the National Natural Science Foundation of China

Natural Science Foundation of Jiangsu Province for Youth

National Key Research and Development Program of China

“Xiaomi Youth Scholar—Innovation and Technology Award”

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/8919/10484988/10431799.pdf?arnumber=10431799

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