48 citations · 115 across the 19 of their papers we have counts for
10 papers · 1 filter
Spiking Neural Networks for fMRI-Based Visual Semantic Decoding
Jiahong Zhang, Jinning Zhao, Sijun Shen +3
Functional magnetic resonance imaging (fMRI)-based visual decoding aims to recover visual information from measured brain activity, commonly by mapping fMRI responses into latent v…
Burst Spiking Neural Networks
Jiahong Zhang, Sijun Shen, Man Yao +5
A central goal of current Spiking Neural Network (SNN) research is to improve their accuracy toward becoming low-power alternatives to Artificial Neural Networks (ANNs). This work…
SpikeMLLM: Spike-based Multimodal Large Language Models via Modality-Specific Temporal Scales and Temporal Compression
Han Xu, Zhiyong Qin, Di Shang +6
Multimodal Large Language Models (MLLMs) have achieved remarkable progress but incur substantial computational overhead and energy consumption during inference, limiting deployment…
Spike-driven Large Language Model
Han Xu, Xuerui Qiu, Baiyu Chen +7
Current Large Language Models (LLMs) are primarily based on large-scale dense matrix multiplications. Inspired by the brain's information processing mechanism, we explore the funda…
Parallel Training in Spiking Neural Networks
Yanbin Huang, Man Yao, Yuqi Pan +5
The bio-inspired integrate-fire-reset mechanism of spiking neurons constitutes the foundation for efficient processing in Spiking Neural Networks (SNNs). Recent progress in large m…
SpikeVoice: High-Quality Text-to-Speech Via Efficient Spiking Neural Network
Kexin Wang, Jiahong Zhang, Yong Ren +4
Brain-inspired Spiking Neural Network (SNN) has demonstrated its effectiveness and efficiency in vision, natural language, and speech understanding tasks, indicating their capacity…
High-Performance Temporal Reversible Spiking Neural Networks with Training Memory and Inference Cost
JiaKui Hu, Man Yao, Xuerui Qiu +6
Multi-timestep simulation of brain-inspired Spiking Neural Networks (SNNs) boost memory requirements during training and increase inference energy cost. Current training methods ca…
Spike-driven Transformer V2: Meta Spiking Neural Network Architecture Inspiring the Design of Next-generation Neuromorphic Chips
Man Yao, Jiakui Hu, Tianxiang Hu +5
Neuromorphic computing, which exploits Spiking Neural Networks (SNNs) on neuromorphic chips, is a promising energy-efficient alternative to traditional AI. CNN-based SNNs are the c…
Inherent Redundancy in Spiking Neural Networks
Man Yao, Jiakui Hu, Guangshe Zhao +4
Spiking Neural Networks (SNNs) are well known as a promising energy-efficient alternative to conventional artificial neural networks. Subject to the preconceived impression that SN…
Spike-driven Transformer
Man Yao, Jiakui Hu, Zhaokun Zhou +4
Spiking Neural Networks (SNNs) provide an energy-efficient deep learning option due to their unique spike-based event-driven (i.e., spike-driven) paradigm. In this paper, we incorp…