33 citations · 38 across the 5 of their papers we have counts for
4 papers · 1 filter
STCSNN: High energy efficiency spike-train level spiking neural networks with spatio-temporal conversion
Changqing Xu, Yi Liu, Yintang Yang
Brain-inspired spiking neuron networks (SNNs) have attracted widespread research interest due to their low power features, high biological plausibility, and strong spatiotemporal i…
Ultra-low Latency Adaptive Local Binary Spiking Neural Network with Accuracy Loss Estimator
Changqing Xu, Yijian Pei, Zili Wu +2
Spiking neural network (SNN) is a brain-inspired model which has more spatio-temporal information processing capacity and computational energy efficiency. However, with the increas…
Ultra-low Latency Spiking Neural Networks with Spatio-Temporal Compression and Synaptic Convolutional Block
Changqing Xu, Yi Liu, Yintang Yang
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…
Direct Training via Backpropagation for Ultra-low Latency Spiking Neural Networks with Multi-threshold
Changqing Xu, Yi Liu, Yintang Yang
Spiking neural networks (SNNs) can utilize spatio-temporal information and have a nature of energy efficiency which is a good alternative to deep neural networks(DNNs). The event-d…