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

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article numbera68
JournalACM Transactions on Embedded Computing Systems
Volume18
Issue number5s
DOIs
Publication statusPublished - 2019 Oct

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture

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