Reduction and IR-drop compensations techniques for reliable neuromorphic computing systems

Published

Conference Paper

© 2014 IEEE. Neuromorphic computing system (NCS) is a promising architecture to combat the well-known memory bottleneck in Von Neumann architecture. The recent breakthrough on memristor devices made an important step toward realizing a low-power, small-footprint NCS on-A-chip. However, the currently low manufacturing reliability of nano-devices and the voltage IR-drop along metal wires and memristors arrays severely limits the scale of me-mristor crossbar based NCS and hinders the design scalability. In this work, we propose a novel system reduction scheme that significantly lowers the required dimension of the memristor crossbars in NCS while maintaining high computing accuracy. An IR-drop compensation technique is also proposed to overcome the adverse impacts of the wire resistance and the sneak-path problem in large memristor crossbar designs. Our simulation results show that the proposed techniques can improve computing accuracy by 27.0% and 38.7% less circuit area compared to the original NCS design.

Full Text

Duke Authors

Cited Authors

  • Liu, B; Li, H; Chen, Y; Li, X; Huang, T; Wu, Q; Barnell, M

Published Date

  • January 5, 2015

Published In

Volume / Issue

  • 2015-January / January

Start / End Page

  • 63 - 70

International Standard Serial Number (ISSN)

  • 1092-3152

International Standard Book Number 13 (ISBN-13)

  • 9781479962785

Digital Object Identifier (DOI)

  • 10.1109/ICCAD.2014.7001330

Citation Source

  • Scopus