Memristor crossbar-based neuromorphic computing system: a case study.

Published

Journal Article

By mimicking the highly parallel biological systems, neuromorphic hardware provides the capability of information processing within a compact and energy-efficient platform. However, traditional Von Neumann architecture and the limited signal connections have severely constrained the scalability and performance of such hardware implementations. Recently, many research efforts have been investigated in utilizing the latest discovered memristors in neuromorphic systems due to the similarity of memristors to biological synapses. In this paper, we explore the potential of a memristor crossbar array that functions as an autoassociative memory and apply it to brain-state-in-a-box (BSB) neural networks. Especially, the recall and training functions of a multianswer character recognition process based on the BSB model are studied. The robustness of the BSB circuit is analyzed and evaluated based on extensive Monte Carlo simulations, considering input defects, process variations, and electrical fluctuations. The results show that the hardware-based training scheme proposed in the paper can alleviate and even cancel out the majority of the noise issue.

Full Text

Duke Authors

Cited Authors

  • Hu, M; Li, H; Chen, Y; Wu, Q; Rose, GS; Linderman, RW

Published Date

  • October 2014

Published In

Volume / Issue

  • 25 / 10

Start / End Page

  • 1864 - 1878

PubMed ID

  • 25291739

Pubmed Central ID

  • 25291739

Electronic International Standard Serial Number (EISSN)

  • 2162-2388

International Standard Serial Number (ISSN)

  • 2162-237X

Digital Object Identifier (DOI)

  • 10.1109/tnnls.2013.2296777

Language

  • eng