Memristor-based approximated computation

Conference Paper

The cessation of Moore's Law has limited further improvements in power efficiency. In recent years, the physical realization of the memristor has demonstrated a promising solution to ultra-integrated hardware realization of neural networks, which can be leveraged for better performance and power efficiency gains. In this work, we introduce a power efficient framework for approximated computations by taking advantage of the memristor-based multilayer neural networks. A programmable memristor approximated computation unit (Memristor ACU) is introduced first to accelerate approximated computation and a memristor-based approximated computation framework with scalability is proposed on top of the Memristor ACU. We also introduce a parameter configuration algorithm of the Memristor ACU and a feedback state tuning circuit to program the Memristor ACU effectively. Our simulation results show that the maximum error of the Memristor ACU for 6 common complex functions is only 1.87% while the state tuning circuit can achieve 12-bit precision. The implementation of HMAX model atop our proposed memristor-based approximated computation framework demonstrates 22× power efficiency improvements than its pure digital implementation counterpart. © 2013 IEEE.

Full Text

Duke Authors

Cited Authors

  • Li, B; Shan, Y; Hu, M; Wang, Y; Chen, Y; Yang, H

Published Date

  • December 11, 2013

Published In

Start / End Page

  • 242 - 247

International Standard Serial Number (ISSN)

  • 1533-4678

International Standard Book Number 13 (ISBN-13)

  • 9781479912353

Digital Object Identifier (DOI)

  • 10.1109/ISLPED.2013.6629302

Citation Source

  • Scopus