IM-Loss: Information Maximization Loss for Spiking Neural Networks

Spiking Neural Network (SNN), recognized as a type of biologically plausible 1 architecture, has recently drawn much research attention. It transmits information 2 by 0 / 1 spikes. This bio-mimetic mechanism of SNN demonstrates extreme energy 3 efficiency since it avoids any multiplications on neuromorphic hardware. However, 4 the forward-passing 0 / 1 spike quantization will cause information loss and accu-5 racy degradation. To deal with this problem, the Information maximization loss 6 (IM-Loss) that aims at maximizing the information flow in the SNN is proposed in 7 the paper. The IM-Loss not only enhances the information expressiveness of an 8 SNN directly but also plays a part of the role of normalization without introducing 9 any additional operations ( e.g. , bias and scaling) in the inference phase. Addition-10 ally, we introduce a novel differentiable spike activity estimation, Evolutionary 11 Surrogate Gradients (ESG) in SNNs. By appointing automatic evolvable surrogate 12 gradients for spike activity function, ESG can ensure sufficient model updates at 13 the beginning and accurate gradients at the end of the training, resulting in both 14 easy convergence and high task performance. Experimental results on both popular 15 non-spiking static and neuromorphic datasets show that the SNN models trained 16 by our method outperform the current state-of-the-art algorithms. 17