35 citations · 49 across the 10 of their papers we have counts for
10 papers
BiSpikCLM: A Spiking Language Model integrating Softmax-Free Spiking Attention and Spike-Aware Alignment Distillation
Sihang Guo, Chenlin Zhou, Jiaqi Wang +3
Spiking Neural Networks (SNNs) offer promising energy-efficient alternatives to large language models (LLMs) due to their event-driven nature and ultra-low power consumption. Howev…
Adaptive Spiking Neurons for Vision and Language Modeling
Chenlin Zhou, Sihang Guo, Jiaqi Wang +5
Regarded as the third generation of neural networks, Spiking Neural Networks (SNNs) have garnered significant traction due to their biological plausibility and energy efficiency. R…
Winner-Take-All Spiking Transformer for Language Modeling
Chenlin Zhou, Sihang Guo, Jiaqi Wang +6
Spiking Transformers, which combine the scalability of Transformers with the sparse, energy-efficient property of Spiking Neural Networks (SNNs), have achieved impressive results i…
Temporal-adaptive Weight Quantization for Spiking Neural Networks
Han Zhang, Qingyan Meng, Jiaqi Wang +3
Weight quantization in spiking neural networks (SNNs) could further reduce energy consumption. However, quantizing weights without sacrificing accuracy remains challenging. In this…
SpikCommander: A High-performance Spiking Transformer with Multi-view Learning for Efficient Speech Command Recognition
Jiaqi Wang, Liutao Yu, Xiongri Shen +6
Spiking neural networks (SNNs) offer a promising path toward energy-efficient speech command recognition (SCR) by leveraging their event-driven processing paradigm. However, existi…
One-Timestep is Enough: Achieving High-performance ANN-to-SNN Conversion via Scale-and-Fire Neurons
Qiuyang Chen, Huiqi Yang, Qingyan Meng +1
Spiking Neural Networks (SNNs) are gaining attention as energy-efficient alternatives to Artificial Neural Networks (ANNs), especially in resource-constrained settings. While ANN-t…
SM-Former: Spiking Symmetric Mixing Branchformer for Brain Auditory Attention Detection
Jiaqi Wang, Zhengyu Ma, Xiongri Shen +7
Auditory attention detection (AAD) aims to decode listeners' focus in complex auditory environments from electroencephalography (EEG) recordings, which is crucial for developing ne…
Efficient Speech Command Recognition Leveraging Spiking Neural Network and Curriculum Learning-based Knowledge Distillation
Jiaqi Wang, Liutao Yu, Liwei Huang +6
The intrinsic dynamics and event-driven nature of spiking neural networks (SNNs) make them excel in processing temporal information by naturally utilizing embedded time sequences a…
Enhancing EEG-to-Text Decoding through Transferable Representations from Pre-trained Contrastive EEG-Text Masked Autoencoder
Jiaqi Wang, Zhenxi Song, Zhengyu Ma +3
Reconstructing natural language from non-invasive electroencephalography (EEG) holds great promise as a language decoding technology for brain-computer interfaces (BCIs). However,…
Spikingformer: A Key Foundation Model for Spiking Neural Networks
Chenlin Zhou, Liutao Yu, Zhaokun Zhou +5
Spiking neural networks (SNNs) offer a promising energy-efficient alternative to artificial neural networks, due to their event-driven spiking computation. However, some foundation…