An EDA framework for large scale hybrid neuromorphic computing systems

Published

Conference Paper

© 2015 ACM. In implementations of neuromorphic computing systems (NCS), memristor and its crossbar topology have been widely used to realize fully connected neural networks. However, many neural networks utilized in real applications often have a sparse connectivity, which is hard to be efficiently mapped to a crossbar structure. Moreover, the scale of the neural networks is normally much larger than that can be offered by the latest integration technology of memristor crossbars. In this work, we propose AutoNCS - an EDA framework that can automate the NCS designs that combine memristor crossbars and discrete synapse modules. The connections of the neural networks are clustered to improve the utilization of the memristor elements in crossbar structures by taking into account the physical design cost of the NCS. Our results show that AutoNCS can substantially enhance the utilization efficiency of memristor crossbars while reducing the wirelength, area and delay of the physical designs of the NCS.

Full Text

Duke Authors

Cited Authors

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

Published Date

  • January 1, 2015

Published In

Volume / Issue

  • 2015-July /

International Standard Serial Number (ISSN)

  • 0738-100X

International Standard Book Number 13 (ISBN-13)

  • 9781450335201

Digital Object Identifier (DOI)

  • 10.1145/2744769.2744795

Citation Source

  • Scopus