Neuromorphic Computing for Graph Analytics

Finding the single-source shortest paths (SSSP) in a weighted graph is a fundamental problem in computer science with many practical applications, including network flow and social network analysis. Unfortunately, fast and efficient SSSP computations still remain a major bottleneck for shared memory systems, even with multiple processing cores. In this work, our aim is to address SSSP and other graph analytics using neuromorphic computing, which is an emerging computing paradigm inspired by the computations in the mammalian brain. In our architecture, nodes of a graph are modeled as integrate-and-fire (IF) neurons, while edges are modeled as synaptic connections. Unlike a conventional neuromorphic system that uses electrical synapses where the synaptic strength of a connection is represented using a weight, we propose a novel approach where the synaptic strength is represented using the speed of propagation of an action potential (spike). Therefore, edge capacities are modeled as axonal delays in our architecture and are modulated by the precise timing of spikes (plasticity). When a spike is injected into a neuron representing the source node, it induces a spike to its neighbors (connected neurons) after a delay, where the delay is proportional to the capacity of the corresponding edge. These neurons in turn induce spikes to their respective neighbors, creating a spiking wavefront that propagates through the architecture.