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

Publication Type
Conference Paper
Book Title
NICE '19 Proceedings of the 7th Annual Neuro-inspired Computational Elements Workshop
Publication Date
Page Number
Publisher Location
United States of America
Conference Name
Neuro-Inspired Computational Elements Workshop (NICE 2019)
Conference Location
Albany, New York, United States of America
Conference Sponsor
Conference Date

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.