1 code implementation • 15 Feb 2024 • Man Yao, Jiakui Hu, Tianxiang Hu, Yifan Xu, Zhaokun Zhou, Yonghong Tian, Bo Xu, Guoqi Li
CNN-based SNNs are the current mainstream of neuromorphic computing.
1 code implementation • ICCV 2023 • Man Yao, Jiakui Hu, Guangshe Zhao, Yaoyuan Wang, Ziyang Zhang, Bo Xu, Guoqi Li
In this work, we pose and focus on three key questions regarding the inherent redundancy in SNNs.
1 code implementation • NeurIPS 2023 • Man Yao, Jiakui Hu, Zhaokun Zhou, Li Yuan, Yonghong Tian, Bo Xu, Guoqi Li
In this paper, we incorporate the spike-driven paradigm into Transformer by the proposed Spike-driven Transformer with four unique properties: 1) Event-driven, no calculation is triggered when the input of Transformer is zero; 2) Binary spike communication, all matrix multiplications associated with the spike matrix can be transformed into sparse additions; 3) Self-attention with linear complexity at both token and channel dimensions; 4) The operations between spike-form Query, Key, and Value are mask and addition.
no code implementations • 20 May 2023 • Man Yao, Yuhong Chou, Guangshe Zhao, Xiawu Zheng, Yonghong Tian, Bo Xu, Guoqi Li
LTH opens up a new path for network pruning.
no code implementations • 22 Nov 2022 • Xiaoshan Wu, Weihua He, Man Yao, Ziyang Zhang, Yaoyuan Wang, Guoqi Li
Spiking neural network is a novel event-based computational paradigm that is considered to be well suited for processing event camera tasks.
no code implementations • 28 Sep 2022 • Man Yao, Guangshe Zhao, Hengyu Zhang, Yifan Hu, Lei Deng, Yonghong Tian, Bo Xu, Guoqi Li
On ImageNet-1K, we achieve top-1 accuracy of 75. 92% and 77. 08% on single/4-step Res-SNN-104, which are state-of-the-art results in SNNs.
1 code implementation • 15 Dec 2021 • Yifan Hu, Lei Deng, Yujie Wu, Man Yao, Guoqi Li
Despite the rapid progress of neuromorphic computing, inadequate capacity and insufficient representation power of spiking neural networks (SNNs) severely restrict their application scope in practice.
no code implementations • ICCV 2021 • Man Yao, Huanhuan Gao, Guangshe Zhao, Dingheng Wang, Yihan Lin, ZhaoXu Yang, Guoqi Li
However, when aggregating individual events into frames with a new higher temporal resolution, existing SNN models do not attach importance to that the serial frames have different signal-to-noise ratios since event streams are sparse and non-uniform.
Ranked #6 on Audio Classification on SHD
no code implementations • 21 Aug 2020 • Dingheng Wang, Bijiao Wu, Guangshe Zhao, Man Yao, Hengnu Chen, Lei Deng, Tianyi Yan, Guoqi Li
Recurrent neural networks (RNNs) are powerful in the tasks oriented to sequential data, such as natural language processing and video recognition.