Shortest Path and Neighborhood Subgraph Extraction on a Spiking Memristive Neuromorphic Implementation

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

doi:10.1145/3320288.3320290

Title: Shortest Path and Neighborhood Subgraph Extraction on a Spiking Memristive Neuromorphic Implementation

Conference · Fri Mar 01 00:00:00 EST 2019

DOI:https://doi.org/10.1145/3320288.3320290· OSTI ID:1559663

^[1];

^[1]; Adnan, Md Musabbir ^[2]; Woong Ku, Bon ^[3]; Kyu Lim, Sung ^[3]; Rose, Garrett ^[2]

ORNL
University of Tennessee (UT)
Georgia Institute of Technology

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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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)

DOE Contract Number:: AC05-00OR22725

OSTI ID:: 1559663

Resource Relation:: Conference: Neuro-Inspired Computational Elements Workshop (NICE 2019) - Albany, New York, United States of America - 3/26/2019 8:00:00 AM-3/28/2019 8:00:00 AM

Country of Publication:: United States

Language:: English

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