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

• URL: http://arxiv.org/abs/2304.05627v2
• Date: Mon, 17 Apr 2023 03:07:53 GMT
• Title: Constructing Deep Spiking Neural Networks from Artificial Neural Networks with Knowledge Distillation
• Authors: Qi Xu, Yaxin Li, Jiangrong Shen, Jian K Liu, Huajin Tang, Gang Pan
• Abstract summary: Spiking neural networks (SNNs) are well known as the brain-inspired models with high computing efficiency. We propose a novel method of constructing deep SNN models with knowledge distillation (KD)
• Score: 20.487853773309563
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spiking neural networks (SNNs) are well known as the brain-inspired models with high computing efficiency, due to a key component that they utilize spikes as information units, close to the biological neural systems. Although spiking based models are energy efficient by taking advantage of discrete spike signals, their performance is limited by current network structures and their training methods. As discrete signals, typical SNNs cannot apply the gradient descent rules directly into parameters adjustment as artificial neural networks (ANNs). Aiming at this limitation, here we propose a novel method of constructing deep SNN models with knowledge distillation (KD) that uses ANN as teacher model and SNN as student model. Through ANN-SNN joint training algorithm, the student SNN model can learn rich feature information from the teacher ANN model through the KD method, yet it avoids training SNN from scratch when communicating with non-differentiable spikes. Our method can not only build a more efficient deep spiking structure feasibly and reasonably, but use few time steps to train whole model compared to direct training or ANN to SNN methods. More importantly, it has a superb ability of noise immunity for various types of artificial noises and natural signals. The proposed novel method provides efficient ways to improve the performance of SNN through constructing deeper structures in a high-throughput fashion, with potential usage for light and efficient brain-inspired computing of practical scenarios.

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