2 code implementations • 21 Mar 2024 • Xinyu Shi, Zecheng Hao, Zhaofei Yu
Based on DSSA, we propose a novel spiking Vision Transformer architecture called SpikingResformer, which combines the ResNet-based multi-stage architecture with our proposed DSSA to improve both performance and energy efficiency while reducing parameters.
no code implementations • 1 Feb 2024 • Zecheng Hao, Xinyu Shi, Zhiyu Pan, Yujia Liu, Zhaofei Yu, Tiejun Huang
Compared to traditional Artificial Neural Network (ANN), Spiking Neural Network (SNN) has garnered widespread academic interest for its intrinsic ability to transmit information in a more biological-inspired and energy-efficient manner.
2 code implementations • 21 Feb 2023 • Zecheng Hao, Jianhao Ding, Tong Bu, Tiejun Huang, Zhaofei Yu
The experimental results show that our proposed method achieves state-of-the-art performance on CIFAR-10, CIFAR-100, and ImageNet datasets.
2 code implementations • 4 Feb 2023 • Zecheng Hao, Tong Bu, Jianhao Ding, Tiejun Huang, Zhaofei Yu
Spiking Neural Networks (SNNs) have received extensive academic attention due to the unique properties of low power consumption and high-speed computing on neuromorphic chips.
1 code implementation • CVPR 2023 • Tong Bu, Jianhao Ding, Zecheng Hao, Zhaofei Yu
Spiking Neural Networks (SNNs) have attracted significant attention due to their energy-efficient properties and potential application on neuromorphic hardware.