  Spiking neural networks are efficient computation models for low-power
environments. Spike-based BP algorithms and ANN-to-SNN (ANN2SNN) conversions
are successful techniques for SNN training. Nevertheless, the spike-base BP
training is slow and requires large memory costs. Though ANN2NN provides a
low-cost way to train SNNs, it requires many inference steps to mimic the
well-trained ANN for good performance. In this paper, we propose a SNN-to-ANN
(SNN2ANN) framework to train the SNN in a fast and memory-efficient way. The
SNN2ANN consists of 2 components: a) a weight sharing architecture between ANN
and SNN and b) spiking mapping units. Firstly, the architecture trains the
weight-sharing parameters on the ANN branch, resulting in fast training and low
memory costs for SNN. Secondly, the spiking mapping units ensure that the
activation values of the ANN are the spiking features. As a result, the
classification error of the SNN can be optimized by training the ANN branch.
Besides, we design an adaptive threshold adjustment (ATA) algorithm to address
the noisy spike problem. Experiment results show that our SNN2ANN-based models
perform well on the benchmark datasets (CIFAR10, CIFAR100, and Tiny-ImageNet).
Moreover, the SNN2ANN can achieve comparable accuracy under 0.625x time steps,
0.377x training time, 0.27x GPU memory costs, and 0.33x spike activities of the
Spike-based BP model.
