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

Conference · Sun Mar 01 04:00:00 EST 2020

DOI:https://doi.org/10.1145/3381755.3381762· OSTI ID:1649307

Kay, Bill ^[1]; Date, Prasanna ^[1]; Schuman, Catherine ^[1]

---

1. ORNL

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.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 1649307
• Country of Publication: United States
• Language: English