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Neuromorphic Graph Algorithms: Extracting Longest Shortest Paths and Minimum Spanning Trees...

by William W Kay, Prasanna A Date, Catherine D Schuman
Publication Type
Conference Paper
Book Title
Proceedings of the Neuro-inspired Computational Elements Workshop
Publication Date
Page Numbers
1 to 6
Publisher Location
Heidelberg, Germany
Conference Name
Neuro-Inspired Computational Elements (NICE)
Conference Location
Heidelberg, Germany
Conference Sponsor
Kirchhoff-Institute for Physics
Conference Date

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.