Achieving lossless accuracy with lossy programming for efficient neural-network training on NVM-based systems

Wei Chen Wang, Yuan Hao Chang, Tei Wei Kuo, Chien Chung Ho, Yu Ming Chang, Hung Sheng Chang

研究成果: Article同行評審

11 引文 斯高帕斯(Scopus)

摘要

Neural networks over conventional computing platforms are heavily restricted by the data volume and performance concerns. While non-volatile memory offers potential solutions to data volume issues, challenges must be faced over performance issues, especially with asymmetric read and write performance. Beside that, critical concerns over endurance must also be resolved before non-volatile memory could be used in reality for neural networks. This work addresses the performance and endurance concerns altogether by proposing a data-aware programming scheme. We propose to consider neural network training jointly with respect to the data-flow and data-content points of view. In particular, methodologies with approximate results over Dual-SET operations were presented. Encouraging results were observed through a series of experiments, where great efficiency and lifetime enhancement is seen without sacrificing the result accuracy.

原文English
文章編號a68
期刊ACM Transactions on Embedded Computing Systems
18
發行號5s
DOIs
出版狀態Published - 2019 10月

All Science Journal Classification (ASJC) codes

  • 軟體
  • 硬體和架構

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