EvtSNN: Event-driven SNN simulator optimized by population and pre-filtering - Mo, Lingfei; Tao, Zhihan (Frontiers in Neuroscience, 2022)
Recently, spiking neural network (SNN) hasnetworks (SNNs) have been widely concernedstudied by researchers due to itstheir biological interpretability and potential application of low power consumption. However, the traditional clock-driven simulators have the problem that the accuracy is limited by the time-step and the lateral inhibition failure. To address this issue, we introduce EvtSNN (Event SNN), a faster SNN event-driven simulator inspired by EDHA (Event-Driven High Accuracy). Two innovations are proposed to accelerate the calculation of event-driven neurons. Firstly, the intermediate results can be reused in population computing without repeated calculations. Secondly, unnecessary peak calculations will be skipped according to thea condition. In the MNIST classification task, EvtSNN took 56s to complete one epoch of unsupervised training and achieved 89.56% accuracy, while EDHA takes 467s642s. In the benchmark experiments, the simulation speed of EvtSNN is 2.9∼14.0 times that of EDHA under different network scales. On the Raspberry Pi Zero 2W embedded platform, it takes 706s to complete the MNIST unsupervised training task with an average power consumption of 1.03 watts.