43 citations · 117 across the 11 of their papers we have counts for
6 papers · 1 filter
Burst Spiking Neural Networks
Jiahong Zhang, Sijun Shen, Man Yao +5
A central goal of current Spiking Neural Network (SNN) research is to improve their accuracy toward becoming low-power alternatives to Artificial Neural Networks (ANNs). This work…
High-Performance Temporal Reversible Spiking Neural Networks with Training Memory and Inference Cost
JiaKui Hu, Man Yao, Xuerui Qiu +6
Multi-timestep simulation of brain-inspired Spiking Neural Networks (SNNs) boost memory requirements during training and increase inference energy cost. Current training methods ca…
Inherent Redundancy in Spiking Neural Networks
Man Yao, Jiakui Hu, Guangshe Zhao +4
Spiking Neural Networks (SNNs) are well known as a promising energy-efficient alternative to conventional artificial neural networks. Subject to the preconceived impression that SN…
Gated Attention Coding for Training High-performance and Efficient Spiking Neural Networks
Xuerui Qiu, Rui-Jie Zhu, Yuhong Chou +3
Spiking neural networks (SNNs) are emerging as an energy-efficient alternative to traditional artificial neural networks (ANNs) due to their unique spike-based event-driven nature.…
Spike-driven Transformer
Man Yao, Jiakui Hu, Zhaokun Zhou +4
Spiking Neural Networks (SNNs) provide an energy-efficient deep learning option due to their unique spike-based event-driven (i.e., spike-driven) paradigm. In this paper, we incorp…
Training Full Spike Neural Networks via Auxiliary Accumulation Pathway
Guangyao Chen, Peixi Peng, Guoqi Li +1
Due to the binary spike signals making converting the traditional high-power multiply-accumulation (MAC) into a low-power accumulation (AC) available, the brain-inspired Spiking Ne…