STCSNN: High energy efficiency spike-train level spiking neural networks with spatio-temporal conversion

• URL: http://arxiv.org/abs/2307.07136v2
• Date: Thu, 5 Sep 2024 05:23:22 GMT
• Title: STCSNN: High energy efficiency spike-train level spiking neural networks with spatio-temporal conversion
• Authors: Changqing Xu, Yi Liu, Yintang Yang,
• Abstract summary: Brain-temporal spiking neuron networks (SNNs) have attracted widespread research interest due to their low power features, high biological plausibility, and strong information processing capability. Although adopting a surrogate (SG) makes the non-differentiability SNN trainable, achieving comparable accuracy for ANNs and keeping low-power features simultaneously is still tricky. In this paper, we proposed an energy-efficient spiking neural network withtemporal conversion, which has low computational cost and high accuracy.
• Score: 4.892303151981707
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Brain-inspired spiking neuron networks (SNNs) have attracted widespread research interest due to their low power features, high biological plausibility, and strong spatiotemporal information processing capability. Although adopting a surrogate gradient (SG) makes the non-differentiability SNN trainable, achieving comparable accuracy for ANNs and keeping low-power features simultaneously is still tricky. In this paper, we proposed an energy-efficient spike-train level spiking neural network with spatio-temporal conversion, which has low computational cost and high accuracy. In the STCSNN, spatio-temporal conversion blocks (STCBs) are proposed to keep the low power features of SNNs and improve accuracy. However, STCSNN cannot adopt backpropagation algorithms directly due to the non-differentiability nature of spike trains. We proposed a suitable learning rule for STCSNNs by deducing the equivalent gradient of STCB. We evaluate the proposed STCSNN on static and neuromorphic datasets, including Fashion-Mnist, Cifar10, Cifar100, TinyImageNet, and DVS-Cifar10. The experiment results show that our proposed STCSNN outperforms the state-of-the-art accuracy on nearly all datasets, using fewer time steps and being highly energy-efficient.

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