A Noise-Based Novel Strategy for Faster SNN Training

Abstract

Spiking neural networks (SNNs) are receiving increasing attention due to their low power consumption and strong bioplausibility. Optimization of SNNs is a challenging task. Two main methods, artificial neural network (ANN)-to-SNN conversion and spike-based backpropagation (BP), both have advantages and limitations. ANN-to-SNN conversion requires a long inference time to approximate the accuracy of ANN, thus diminishing the benefits of SNN. With spike-based BP, training high-precision SNNs typically consumes dozens of times more computational resources and time than their ANN counterparts. In this letter, we propose a novel SNN training approach that combines the benefits of the two methods. We first train a single-step SNN(T = 1) by approximating the neural potential distribution with random noise, then convert the single-step SNN(T = 1) to a multistep SNN(T = N) losslessly. The introduction of gaussian distributed noise leads to a significant gain in accuracy after conversion. The results show that our method considerably reduces the training and inference times of SNNs while maintaining their high accuracy. Compared to the previous two methods, ours can reduce training time by 65% to 75% and achieves more than 100 times faster inference speed. We also argue that the neuron model augmented with noise makes it more bioplausible.