  Spiking neural networks (SNNs) are known as a typical kind of brain-inspired
models with their unique features of rich neuronal dynamics, diverse coding
schemes and low power consumption properties. How to obtain a high-accuracy
model has always been the main challenge in the field of SNN. Currently, there
are two mainstream methods, i.e., obtaining a converted SNN through converting
a well-trained Artificial Neural Network (ANN) to its SNN counterpart or
training an SNN directly. However, the inference time of a converted SNN is too
long, while SNN training is generally very costly and inefficient. In this
work, a new SNN training paradigm is proposed by combining the concepts of the
two different training methods with the help of the pretrain technique and
BP-based deep SNN training mechanism. We believe that the proposed paradigm is
a more efficient pipeline for training SNNs. The pipeline includes pipeS for
static data transfer tasks and pipeD for dynamic data transfer tasks. SOTA
results are obtained in a large-scale event-driven dataset ES-ImageNet. For
training acceleration, we achieve the same (or higher) best accuracy as similar
LIF-SNNs using 1/10 training time on ImageNet-1K and 2/5 training time on
ES-ImageNet and also provide a time-accuracy benchmark for a new dataset
ES-UCF101. These experimental results reveal the similarity of the functions of
parameters between ANNs and SNNs and also demonstrate the various potential
applications of this SNN training pipeline.
