A practical low-power memristor-based analog neural branch predictor

Conference Paper

Recently, the discovery of memristor brought the promise of high density, low energy, and combined memory/arithmetic capability into computing. This paper demonstrates a practical neural branch predictor based on memristor. By using analog computation techniques, as well as exploiting the accuracy tolerance of branch prediction, our design is able to efficiently realize a neural prediction algorithm. Compared to the digital counterpart, our method achieves significant energy reduction while maintaining a better prediction accuracy and a higher IPC. Our approach also reduces the resource and energy required by an alternative design. © 2013 IEEE.

Full Text

Duke Authors

Cited Authors

  • Wang, J; Tim, Y; Wong, WF; Li, HH

Published Date

  • December 11, 2013

Published In

Start / End Page

  • 175 - 180

International Standard Serial Number (ISSN)

  • 1533-4678

International Standard Book Number 13 (ISBN-13)

  • 9781479912353

Digital Object Identifier (DOI)

  • 10.1109/ISLPED.2013.6629290

Citation Source

  • Scopus