no code implementations • NeurIPS 2023 • Qi Xu, Yuyuan Gao, Jiangrong Shen, Yaxin Li, Xuming Ran, Huajin Tang, Gang Pan
Spiking neural networks (SNNs) serve as one type of efficient model to process spatio-temporal patterns in time series, such as the Address-Event Representation data collected from Dynamic Vision Sensor (DVS).
no code implementations • 11 Sep 2023 • Huajin Tang, Pengjie Gu, Jayawan Wijekoon, MHD Anas Alsakkal, ZiMing Wang, Jiangrong Shen, Rui Yan
Neuromorphic computing holds the promise to achieve the energy efficiency and robust learning performance of biological neural systems.
no code implementations • 6 Jun 2023 • Jiangrong Shen, Qi Xu, Jian K. Liu, Yueming Wang, Gang Pan, Huajin Tang
To take full advantage of low power consumption and improve the efficiency of these models further, the pruning methods have been explored to find sparse SNNs without redundancy connections after training.
no code implementations • 19 Apr 2023 • Qi Xu, Yaxin Li, Xuanye Fang, Jiangrong Shen, Jian K. Liu, Huajin Tang, Gang Pan
The proposed method explores a novel dynamical way for structure learning from scratch in SNNs which could build a bridge to close the gap between deep learning and bio-inspired neural dynamics.
no code implementations • 17 Apr 2023 • Di Hong, Jiangrong Shen, Yu Qi, Yueming Wang
A conversion scheme is proposed to obtain competitive accuracy by mapping trained ANNs' parameters to SNNs with the same structures.
no code implementations • CVPR 2023 • Qi Xu, Yaxin Li, Jiangrong Shen, Jian K Liu, Huajin Tang, Gang Pan
Spiking neural networks (SNNs) are well known as the brain-inspired models with high computing efficiency, due to a key component that they utilize spikes as information units, close to the biological neural systems.