Spike-based primitives for graph algorithms

TL;DR

This paper explores using neuromorphic computing platforms, specifically spiking neurons, to implement fundamental graph algorithms, showcasing hardware-agnostic methods that leverage neuron dynamics for graph processing.

Contribution

It introduces novel spike-based primitives for graph algorithms, demonstrating their implementation across different neuromorphic hardware configurations.

Findings

Hardware-agnostic spike-based graph routines demonstrated

Multiple implementations utilizing static and plastic synapses

Potential for neuromorphic platforms in graph analysis applications

Abstract

In this paper we consider graph algorithms and graphical analysis as a new application for neuromorphic computing platforms. We demonstrate how the nonlinear dynamics of spiking neurons can be used to implement low-level graph operations. Our results are hardware agnostic, and we present multiple versions of routines that can utilize static synapses or require synapse plasticity.