14 citations · 15 across the 5 of their papers we have counts for
4 papers · 1 filter
Large Language Model-Guided Evolutionary Discovery of Native Neural Architectures for Spiking Sequence Modeling
Ruoyu Zhao, Jiaqi Wu, Chenyu Zhu +1
Spiking neural networks (SNNs) offer low-energy sequence modeling through sparse, event-driven computation. However, interactions among spike encoding, neuronal dynamics, and infor…
SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mamba
Yulong Huang, Jianxiong Tang, Chao Wang +5
Large Language Models (LLMs) have achieved remarkable performance across tasks but remain energy-intensive due to dense matrix operations. Spiking neural networks (SNNs) improve en…
SPikE-SSM: A Sparse, Precise, and Efficient Spiking State Space Model for Long Sequences Learning
Yan Zhong, Ruoyu Zhao, Chao Wang +4
Spiking neural networks (SNNs) provide an energy-efficient solution by utilizing the spike-based and sparse nature of biological systems. Since the advent of Transformers, SNNs hav…
Evolutionary Spiking Neural Networks: A Survey
Shuaijie Shen, Rui Zhang, Chao Wang +6
Spiking neural networks (SNNs) are gaining increasing attention as potential computationally efficient alternatives to traditional artificial neural networks(ANNs). However, the un…