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Spiking neural networks (SNNs) are well known as the brain-inspired modelswith high computing efficiency, due to a key component that they utilize spikesas information units, close to the biological neural systems. Although spikingbased models are energy efficient by taking advantage of discrete spikesignals, their performance is limited by current network structures and theirtraining methods. As discrete signals, typical SNNs cannot apply the gradientdescent rules directly into parameters adjustment as artificial neural networks(ANNs). Aiming at this limitation, here we propose a novel method ofconstructing deep SNN models with knowledge distillation (KD) that uses ANN asteacher model and SNN as student model. Through ANN-SNN joint trainingalgorithm, the student SNN model can learn rich feature information from theteacher ANN model through the KD method, yet it avoids training SNN fromscratch when communicating with non-differentiable spikes. Our method can notonly build a more efficient deep spiking structure feasibly and reasonably, butuse few time steps to train whole model compared to direct training or ANN toSNN methods. More importantly, it has a superb ability of noise immunity forvarious types of artificial noises and natural signals. The proposed novelmethod provides efficient ways to improve the performance of SNN throughconstructing deeper structures in a high-throughput fashion, with potentialusage for light and efficient brain-inspired computing of practical scenarios.

Constructing Deep Spiking Neural Networks from Artificial Neural Networks with Knowledge Distillation