14 citations · 15 across the 5 of their papers we have counts for
6 papers
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…
Neural Spectral Capacity: Measuring and Designing Architectures from Network Specification Alone
Chenyu Zhu, Ruoyu Zhao, Zhichao Lu
Modern Transformer design and compression both reduce to allocating capacity under a budget. The standard scalars for these decisions, #Params and #FLOPs, capture size and compute…
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…
SpikingSSMs: Learning Long Sequences with Sparse and Parallel Spiking State Space Models
Shuaijie Shen, Chao Wang, Renzhuo Huang +5
Known as low energy consumption networks, spiking neural networks (SNNs) have gained a lot of attention within the past decades. While SNNs are increasing competitive with artifici…
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…