Spiking neural networks (SNNs) have shown clear advantages over traditional
artificial neural networks (ANNs) for low latency and high computational
efficiency, due to their event-driven nature and sparse communication. However,
the training of deep SNNs is not straightforward. In this paper, we propose a
novel ANN-to-SNN conversion and layer-wise learning framework for rapid and
efficient pattern recognition, which is referred to as progressive tandem
learning of deep SNNs. By studying the equivalence between ANNs and SNNs in the
discrete representation space, a primitive network conversion method is
introduced that takes full advantage of spike count to approximate the
activation value of analog neurons. To compensate for the approximation errors
arising from the primitive network conversion, we further introduce a
layer-wise learning method with an adaptive training scheduler to fine-tune the
network weights. The progressive tandem learning framework also allows hardware
constraints, such as limited weight precision and fan-in connections, to be
progressively imposed during training. The SNNs thus trained have demonstrated
remarkable classification and regression capabilities on large-scale object
recognition, image reconstruction, and speech separation tasks, while requiring
at least an order of magnitude reduced inference time and synaptic operations
than other state-of-the-art SNN implementations. It, therefore, opens up a
myriad of opportunities for pervasive mobile and embedded devices with a
limited power budget.

本文提出了一种称为渐进串联深度 SNN 的 ANN 到 SNN 的转换和分层学习框架，该框架利用脉冲计数以逼近模拟神经元的激活值，以及一种适应性训练调度器细化网络权重，其在大规模物体识别、图像重建和语音分离任务中能够展示出出色的分类和回归能力，同时需要的推理时间和突触操作比其他最先进的 SNN 实现少了至少一个数量级，因此它为功率有限的移动和嵌入式设备提供了无数机会。