4 papers
Temporal-adaptive Weight Quantization for Spiking Neural Networks
Han Zhang, Qingyan Meng, Jiaqi Wang +3
Weight quantization in spiking neural networks (SNNs) could further reduce energy consumption. However, quantizing weights without sacrificing accuracy remains challenging. In this…
One-Timestep is Enough: Achieving High-performance ANN-to-SNN Conversion via Scale-and-Fire Neurons
Qiuyang Chen, Huiqi Yang, Qingyan Meng +1
Spiking Neural Networks (SNNs) are gaining attention as energy-efficient alternatives to Artificial Neural Networks (ANNs), especially in resource-constrained settings. While ANN-t…
A Self-Ensemble Inspired Approach for Effective Training of Binary-Weight Spiking Neural Networks
Qingyan Meng, Mingqing Xiao, Zhengyu Ma +3
Spiking Neural Networks (SNNs) are a promising approach to low-power applications on neuromorphic hardware due to their energy efficiency. However, training SNNs is challenging bec…
E2ATST: A Temporal-Spatial Optimized Energy-Efficient Architecture for Training Spiking Transformer
Yunhao Ma, Yanyu Lin, Mingjing Li +9
(1) Pengcheng Laboratory, (2) Southern University of Science and Technology, (3) Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, (4) University of Chinese…