Shortest Path and Neighborhood Subgraph Extraction on a Spiking Memristive Neuromorphic Implementation
Abstract: Spiking neuromorphic computers (SNCs) are promising as a post Moore's law technology because of their potential for very low power computation. SNCs have primarily been demonstrated on machine learning applications, but they can also be used for applications beyond machine learning. Here, we demonstrate two graph problems (shortest path and neighborhood subgraph extraction) that can be solved using SNCs. We estimate the performance of a memristive SNC for these applications on three real-world graphs.
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