BitXpro: Regularity-Aware Hardware Runtime Pruning for Deep Neural Networks-Reference-Cited by-同舟云学术

BitXpro: Regularity-Aware Hardware Runtime Pruning for Deep Neural Networks

Published:2023-01 Issue:1 Volume:31 Page:90-103
ISSN:1063-8210
Container-title:IEEE Transactions on Very Large Scale Integration (VLSI) Systems
language:
Short-container-title:IEEE Trans. VLSI Syst.

Author:

Li Hongyan¹,Lu Hang²^ORCID,Wang Haoxuan¹,Deng Shengji³,Li Xiaowei¹^ORCID

Affiliation:

1. State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

2. State Key Laboratory of Computer Architecture, Institute of Computing Technology, the Zhongguancun Laboratory, and the Shanghai Innovation Center for Processor Technologies (SHIC), Chinese Academy of Sciences, Beijing, China

3. Second Research Institute of the Civil Aviation Administration of China (CAAC), Beijing, China

Funder

National Natural Science Foundation of China

Youth Innovation Promotion Association of Chinese Academy of Sciences

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Hardware and Architecture,Software

Link

http://xplorestaging.ieee.org/ielx7/92/9998453/09956969.pdf?arnumber=9956969

Reference45 articles.

1. ImageNet: A large-scale hierarchical image database

2. Tetris

3. Network trimming: A data-driven neuron pruning approach towards efficient deep architectures;hu;arXiv 1607 03250,2016

4. ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression

5. Deformable 3D Convolution for Video Super-Resolution

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