RAM: Exploiting Restrained and Approximate Management for Enabling Neural Network Training on NVM-based Systems

Chien Chung Ho, Wei Chen Wang, Szu Yu Chen, Yung Chun Li, Kun Chi Chiang

研究成果: Conference contribution

1 引文 斯高帕斯(Scopus)

摘要

Training neural networks (NN) over conventional DRAM-based edge devices are highly restricted due to DRAM's leakage-power and limited-density properties. The non-volatile memory (NVM) reveals its potential of providing solutions with the high-density and nearly-zero leakage power features; however, it could lead to severe performance and lifetime issues. This paper focuses on exploring the NVM-aware neural network training design to mitigate performance and lifetime issues caused by the conventional NN approaches. Especially, we aim at exploiting the restrained-and-approximate managements to leverage insignificant data on performance and lifetime optimization. The encouraging results were observed with the comparable validation accuracy in the evaluations.

原文English
主出版物標題Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing, SAC 2022
發行者Association for Computing Machinery
頁面116-123
頁數8
ISBN(電子)9781450387132
DOIs
出版狀態Published - 2022 4月 25
事件37th ACM/SIGAPP Symposium on Applied Computing, SAC 2022 - Virtual, Online
持續時間: 2022 4月 252022 4月 29

出版系列

名字Proceedings of the ACM Symposium on Applied Computing

Conference

Conference37th ACM/SIGAPP Symposium on Applied Computing, SAC 2022
城市Virtual, Online
期間22-04-2522-04-29

All Science Journal Classification (ASJC) codes

  • 軟體

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