Bill Kay and Prasanna Date and Catherine Schuman

March, 2020

NICE: Neuro-Inspired Computational Elements Workshop

https://niceworkshop.org/nice-2020/

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Abstract

Neuromorphic computing is poised to become a promising computing paradigm in the post Moore’s law era due to its extremely low power usage and inherent parallelism. Traditionally speaking, a majority of the use cases for neuromorphic systems have been in the field of machine learning. In order to expand their usability, it is imperative that neuromorphic systems be used for non-machine learning tasks as well. The structural aspects of neuromorphic systems (i.e., neurons and synapses) are similar to those of graphs (i.e., nodes and edges), However, it is not obvious how graph algorithms would translate to their neuromorphic counterparts. In this work, we propose a preprocessing technique that introduces fractional offsets on the synaptic delays of neuromorphic graphs in order to break ties. This technique, in turn, enables two graph algorithms: longest shortest path extraction and minimum spanning trees.

Citation Information

Text

author     B. Kay and P. Date and C. Schuman
title      Neuromorphic Graph Algorithms: Extracting Longest Shortest Paths 
           and Minimum Spanning Trees
booktitle  NICE: Neuro-Inspired Computational Elements Workshop
year       2020
where      http://neuromorphic.eecs.utk.edu/publications/2020-03-16-neuromorphic-graph-algorithms-extracting-longest-shortest-paths-and-minimum-spanning-trees

Bibtex

@INPROCEEDINGS{kds:20:nga,
    author = "B. Kay and P. Date and C. Schuman",
    title = "Neuromorphic Graph Algorithms: Extracting Longest Shortest Paths 
               and Minimum Spanning Trees",
    booktitle = "NICE: Neuro-Inspired Computational Elements Workshop",
    year = "2020",
    where = "http://neuromorphic.eecs.utk.edu/publications/2020-03-16-neuromorphic-graph-algorithms-extracting-longest-shortest-paths-and-minimum-spanning-trees"
}