Event-Driven Learning for Spiking Neural Networks

no code implementations • 1 Mar 2024 • Wenjie Wei, Malu Zhang, Jilin Zhang, Ammar Belatreche, Jibin Wu, Zijing Xu, Xuerui Qiu, Hong Chen, Yang Yang, Haizhou Li

Specifically, we introduce two novel event-driven learning methods: the spike-timing-dependent event-driven (STD-ED) and membrane-potential-dependent event-driven (MPD-ED) algorithms.

OR Residual Connection Achieving Comparable Accuracy to ADD Residual Connection in Deep Residual Spiking Neural Networks

1 code implementation • 11 Nov 2023 • Yimeng Shan, Xuerui Qiu, Rui-Jie Zhu, Ruike Li, Meng Wang, Haicheng Qu

Spiking Neural Networks (SNNs) have garnered substantial attention in brain-like computing for their biological fidelity and the capacity to execute energy-efficient spike-driven operations.

Quantization

Tensor Decomposition Based Attention Module for Spiking Neural Networks

1 code implementation • 23 Oct 2023 • Haoyu Deng, Ruijie Zhu, Xuerui Qiu, Yule Duan, Malu Zhang, LiangJian Deng

Then, in AMC, we exploit the inverse procedure of the tensor decomposition process to combine the three tensors into the attention map using a so-called connecting factor.

Tensor Decomposition

Gated Attention Coding for Training High-performance and Efficient Spiking Neural Networks

1 code implementation • 12 Aug 2023 • Xuerui Qiu, Rui-Jie Zhu, Yuhong Chou, Zhaorui Wang, Liang-Jian Deng, Guoqi Li

Experiments on CIFAR10/100 and ImageNet datasets demonstrate that GAC achieves state-of-the-art accuracy with remarkable efficiency.

Efficient Neural Network Image Classification