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

doi:10.1145/3320288.3320290

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

Quantum Computational Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-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 language	English
Title of host publication	Proceedings of the 2019 7th Annual Neuro-Inspired Computational Elements Workshop, NICE 2019
Publisher	Association for Computing Machinery
ISBN (Electronic)	9781450361231
DOIs	https://doi.org/10.1145/3320288.3320290
State	Published - Mar 26 2019
Event	7th Annual Neuro-Inspired Computational Elements Workshop, NICE 2019 - Albany, United States Duration: Mar 26 2019 → Mar 28 2019

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	7th Annual Neuro-Inspired Computational Elements Workshop, NICE 2019
Country/Territory	United States
City	Albany
Period	03/26/19 → 03/28/19

Funding

Notice: This manuscript has been authored in part by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Funders	Funder number
U. S. Department of Energy
U.S. Department of Energy
Office of Science
Advanced Scientific Computing Research	DE-AC05-00OR22725
Oak Ridge National Laboratory

Keywords

Graph algorithms
Memristors
Neighborhood
Neuromorphic computing
Shortest path

Access to Document

10.1145/3320288.3320290

Cite this

Schuman, C. D., Musabbir Adnan, M., Hamilton, K., Ku, B. W., Rose, G. S., Mintz, T., & Lim, S. K. (2019). Shortest path and neighborhood subgraph extraction on a spiking memristive neuromorphic implementation. In Proceedings of the 2019 7th Annual Neuro-Inspired Computational Elements Workshop, NICE 2019 Article a3 (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3320288.3320290

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title = "Shortest path and neighborhood subgraph extraction on a spiking memristive neuromorphic implementation",

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.",

keywords = "Graph algorithms, Memristors, Neighborhood, Neuromorphic computing, Shortest path",

author = "Schuman, {Catherine D.} and {Musabbir Adnan}, Md and Kathleen Hamilton and Ku, {Bon Woong} and Rose, {Garrett S.} and Tiffany Mintz and Lim, {Sung Kyu}",

note = "Publisher Copyright: {\textcopyright} 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM.; 7th Annual Neuro-Inspired Computational Elements Workshop, NICE 2019 ; Conference date: 26-03-2019 Through 28-03-2019",

year = "2019",

month = mar,

day = "26",

doi = "10.1145/3320288.3320290",

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Schuman, CD, Musabbir Adnan, M, Hamilton, K, Ku, BW, Rose, GS, Mintz, T & Lim, SK 2019, Shortest path and neighborhood subgraph extraction on a spiking memristive neuromorphic implementation. in Proceedings of the 2019 7th Annual Neuro-Inspired Computational Elements Workshop, NICE 2019., a3, ACM International Conference Proceeding Series, Association for Computing Machinery, 7th Annual Neuro-Inspired Computational Elements Workshop, NICE 2019, Albany, United States, 03/26/19. https://doi.org/10.1145/3320288.3320290

Shortest path and neighborhood subgraph extraction on a spiking memristive neuromorphic implementation. / Schuman, Catherine D.; Musabbir Adnan, Md; Hamilton, Kathleen et al.
Proceedings of the 2019 7th Annual Neuro-Inspired Computational Elements Workshop, NICE 2019. Association for Computing Machinery, 2019. a3 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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.

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Schuman CD, Musabbir Adnan M, Hamilton K, Ku BW, Rose GS, Mintz T et al. Shortest path and neighborhood subgraph extraction on a spiking memristive neuromorphic implementation. In Proceedings of the 2019 7th Annual Neuro-Inspired Computational Elements Workshop, NICE 2019. Association for Computing Machinery. 2019. a3. (ACM International Conference Proceeding Series). doi: 10.1145/3320288.3320290

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

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