Shortest path and neighborhood subgraph extraction on a spiking memristive neuromorphic implementation

Catherine D. Schuman, Md Musabbir Adnan, Kathleen Hamilton, Bon Woong Ku, Garrett S. Rose, Tiffany Mintz, Sung Kyu Lim

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Spiking neuromorphic computers (SNCs) are promising as a post Moore's law technology partly because of their potential for very low power computation. SNCs have primarily been demonstrated on machine learning and neural network applications, but they can also be used for applications beyond machine learning that can leverage SNC properties such as massively parallel computation and collocated processing and memory. Here, we demonstrate two graph problems (shortest path and neighborhood subgraph extraction) that can be solved using SNCs. We discuss the approach for mapping these applications to an SNC. We also estimate the performance of a memristive SNC for these applications on three real-world graphs.

Original languageEnglish
Title of host publicationProceedings of the 2019 7th Annual Neuro-Inspired Computational Elements Workshop, NICE 2019
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450361231
DOIs
StatePublished - Mar 26 2019
Event7th Annual Neuro-Inspired Computational Elements Workshop, NICE 2019 - Albany, United States
Duration: Mar 26 2019Mar 28 2019

Publication series

NameACM International Conference Proceeding Series

Conference

Conference7th Annual Neuro-Inspired Computational Elements Workshop, NICE 2019
Country/TerritoryUnited States
CityAlbany
Period03/26/1903/28/19

Bibliographical note

Publisher Copyright:
© 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM.

Keywords

  • Graph algorithms
  • Memristors
  • Neighborhood
  • Neuromorphic computing
  • Shortest path

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