Ultra-low Latency Spiking Neural Networks with Spatio-Temporal Compression and Synaptic Convolutional Block

• URL: http://arxiv.org/abs/2203.10006v1
• Date: Fri, 18 Mar 2022 15:14:13 GMT
• Title: Ultra-low Latency Spiking Neural Networks with Spatio-Temporal Compression and Synaptic Convolutional Block
• Authors: Changqing Xu, Yi Liu, Yintang Yang
• Abstract summary: Spiking neural networks (SNNs) have neuro-temporal information capability, low processing feature, and high biological plausibility. Neuro-MNIST, CIFAR10-S, DVS128 gesture datasets need to aggregate individual events into frames with a higher temporal resolution for event stream classification. We propose a processing-temporal compression method to aggregate individual events into a few time steps of NIST current to reduce the training and inference latency.
• Score: 4.081968050250324
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spiking neural networks (SNNs), as one of the brain-inspired models, has spatio-temporal information processing capability, low power feature, and high biological plausibility. The effective spatio-temporal feature makes it suitable for event streams classification. However, neuromorphic datasets, such as N-MNIST, CIFAR10-DVS, DVS128-gesture, need to aggregate individual events into frames with a new higher temporal resolution for event stream classification, which causes high training and inference latency. In this work, we proposed a spatio-temporal compression method to aggregate individual events into a few time steps of synaptic current to reduce the training and inference latency. To keep the accuracy of SNNs under high compression ratios, we also proposed a synaptic convolutional block to balance the dramatic change between adjacent time steps. And multi-threshold Leaky Integrate-and-Fire (LIF) with learnable membrane time constant is introduced to increase its information processing capability. We evaluate the proposed method for event streams classification tasks on neuromorphic N-MNIST, CIFAR10-DVS, DVS128 gesture datasets. The experiment results show that our proposed method outperforms the state-of-the-art accuracy on nearly all datasets, using fewer time steps.

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