48 citations · 62 across the 10 of their papers we have counts for
6 papers · 1 filter
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…
Spatial-Temporal Search for Spiking Neural Networks
Kaiwei Che, Zhaokun Zhou, Li Yuan +3
Spiking Neural Networks (SNNs) are considered as a potential candidate for the next generation of artificial intelligence with appealing characteristics such as sparse computation…
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…
Accelerated physical emulation of Bayesian inference in spiking neural networks
Akos F. Kungl, Sebastian Schmitt, Johann Klähn +21
The massively parallel nature of biological information processing plays an important role for its superiority to human-engineered computing devices. In particular, it may hold the…
Spiking neurons with short-term synaptic plasticity form superior generative networks
Luziwei Leng, Roman Martel, Oliver Breitwieser +5
Spiking networks that perform probabilistic inference have been proposed both as models of cortical computation and as candidates for solving problems in machine learning. However,…