EDA challenges for memristor-crossbar based neuromorphic computing

Conference Paper

The increasing gap between the high data processing capability of modern computing systems and the limited memory bandwidth motivated the recent significant research on neuromorphic computing systems (NCS), which are inspired from the working mechanism of human brains. Discovery of memristor further accelerates engineering realization of NCS by leveraging the similarity between synaptic connections in neural networks and programming weight of the memristor. However, to achieve a stable large-scale NCS for practical applications, many essential EDA design challenges still need to be overcome especially the state-of-the-art memristor crossbar structure is adopted. In this paper, we summarize some of our recent published works about enhancing the design robustness and efficiency of memristor crossbar based NCS. The experiments show that the impacts of noises generated by process variations and the IR-drop over the crossbar can be effectively suppressed by our noise-eliminating training method and IR-drop compensation technique. Moreover, our network clustering techniques can alleviate the challenges of limited crossbar scale and routing congestion in NCS implementations.

Full Text

Duke Authors

Cited Authors

  • Liu, B; Wen, W; Chen, Y; Li, X; Wu, CR; Ho, TY

Published Date

  • May 20, 2015

Published In

  • Proceedings of the Acm Great Lakes Symposium on Vlsi, Glsvlsi

Volume / Issue

  • 20-22-May-2015 /

Start / End Page

  • 185 - 188

International Standard Book Number 13 (ISBN-13)

  • 9781450334747

Digital Object Identifier (DOI)

  • 10.1145/2742060.2743754

Citation Source

  • Scopus