We present a clock-driven Spiking Neural Network simulator which is up to 3x faster than the state of the art while, at the same time, being more general and requiring less programming effort on both the user's and maintainer's side. This is made possible by designing our pipeline around "work queues" which act as interfaces between stages and greatly reduce implementation complexity. We evaluate our work using three well-established SNN models on a series of benchmarks.
@article{arxiv.1912.07423,
title = {Faster and Simpler SNN Simulation with Work Queues},
author = {Dennis Bautembach and Iason Oikonomidis and Nikolaos Kyriazis and Antonis Argyros},
journal= {arXiv preprint arXiv:1912.07423},
year = {2021}
}