  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.
