  Spiking neural networks (SNNs) are receiving increasing attention due to
their low power consumption and strong bio-plausibility. Optimization of SNNs
is a challenging task. Two main methods, artificial neural network (ANN)-to-SNN
conversion and spike-based backpropagation (BP), both have their advantages and
limitations. For ANN-to-SNN conversion, it 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
paper, 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 multi-step 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%-75% and achieves more than
100 times faster inference speed. We also argue that the neuron model augmented
with noise makes it more bio-plausible.
