Hardware-Friendly Progressive Pruning Framework for CNN Model Compression using Universal Pattern Sets

Wei Cheng Chou, Cheng Wei Huang, Juinn Dar Huang

研究成果: Conference contribution同行評審

1 引文 斯高帕斯(Scopus)

摘要

Pattern-based weight pruning on CNNs has been proven an effective model reduction technique. In this paper, we first present how to select hardware-friendly pruning pattern sets that are universal to various models. We then propose a progressive pruning framework, which produces more globally optimized outcomes. Moreover, to the best of our knowledge, this is the first paper dealing with the pruning issue of the first and also the most sensitive layer of a CNN model through a two-staged pruning strategy. Experiment results show that the proposed framework achieves 2.25x/2x computation/model reduction while minimizing the accuracy loss.

原文English
主出版物標題2022 International Symposium on VLSI Design, Automation and Test, VLSI-DAT 2022 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781665409216
DOIs
出版狀態Published - 2022
事件2022 International Symposium on VLSI Design, Automation and Test, VLSI-DAT 2022 - Hsinchu, Taiwan
持續時間: 18 4月 202221 4月 2022

出版系列

名字2022 International Symposium on VLSI Design, Automation and Test, VLSI-DAT 2022 - Proceedings

Conference

Conference2022 International Symposium on VLSI Design, Automation and Test, VLSI-DAT 2022
國家/地區Taiwan
城市Hsinchu
期間18/04/2221/04/22

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