부산대학교 도서관

This article presents a novel approach for simulating Spiking Neural Networks (SNNs) that is based on timed dataflow graphs. Whereas conventional SNN simulators compute changes in spiking neuron variables at each time step, the proposed simulation approach focuses on evaluating spike timings. This focus on evaluating when a dataflow actor (spiking neuron) reaches a new spike contributes to making spike evaluation an event-driven computation. The resulting event-driven simulation approach avoids unnecessary computations at time steps that lie between spiking events. This optimization is achieved while avoiding the large overheads associated with lookup tables that are incurred in existing event-driven approaches. Our results show identical spiking behavior compared to simulation using a conventional (time-based) simulator while providing significant improvement in execution time.