2 citations · 4 across the 18 of their papers we have counts for
20 papers
SpikeOcc: Towards Resilient Multimodal 3D Occupancy Perception via Spiking Experts
Yiding Sun, Zijie Xu, Hongqiang Lin +7
Multimodal 3D occupancy prediction underpins safe autonomous driving through comprehensive scene understanding. However, existing methods have largely pursued higher accuracy throu…
Reducing ANN-SNN Conversion Error via Residual Membrane Potential Alignment
Zirui Chen, Zihan Huang, Tong Bu +3
Spiking Neural Networks (SNNs) serve as core architectures for neuromorphic computing thanks to event-driven operation and ultra-low power consumption. Direct SNN training is hinde…
AS-FedBridge: Pseudo-Spike Bridge Distillation for Heterogeneous ANN-SNN Federated Learning
Shengyang Li, Yiting Dong, Liuyang Song +5
Federated learning enables collaborative model training across distributed edge devices while strictly preserving data privacy. To facilitate practical deployment on resource-const…
Uncertainty-Aware Token Importance Estimation in Spiking Transformers
Wenxuan Liu, Zecheng Hao, Tong Bu +2
Spiking transformers have shown strong potential for neuromorphic vision, yet their token processing across multiple spiking steps still introduces substantial redundancy and infer…
GemS-T: Multi-Dimensional Grouping for Ultra-High Energy Efficiency in Spiking Transformer
Zecheng Hao, Shenghao Xie, Kang Chen +3
Spiking Neural Networks (SNNs) offer superior energy efficiency over Artificial Neural Networks (ANNs). However, they encounter significant deficiencies in training and inference m…
Brain-Inspired Multimodal Spiking Neural Network for Image-Text Retrieval
Xintao Zong, Xian Zhong, Wenxuan Liu +3
Spiking neural networks (SNNs) have recently shown strong potential in unimodal visual and textual tasks, yet building a directly trained, low-energy, and high-performance SNN for…
Latency Coding for Efficient and Low-Latency Deep Spiking Neural Networks
Yi Lu, Jianhao Ding, Zhaofei Yu
Spiking neural networks (SNNs) offer a biologically inspired computing paradigm with significant potential for energy-efficient neural processing. Among neural coding schemes of SN…
General Self-Prediction Enhancement for Spiking Neurons
Zihan Huang, Zijie Xu, Yihan Huang +7
Spiking Neural Networks (SNNs) are highly energy-efficient due to event-driven, sparse computation, but their training is challenged by spike non-differentiability and trade-offs a…
Error Amplification Limits ANN-to-SNN Conversion in Continuous Control
Zijie Xu, Zihan Huang, Yiting Dong +3
Spiking Neural Networks (SNNs) can achieve competitive performance by converting already existing well-trained Artificial Neural Networks (ANNs), avoiding further costly training.…
Unleashing Temporal Capacity of Spiking Neural Networks through Spatiotemporal Separation
Yiting Dong, Zhaofei Yu, Jianhao Ding +2
Spiking Neural Networks (SNNs) are considered naturally suited for temporal processing, with membrane potential propagation widely regarded as the core temporal modeling mechanism.…
PredNext: Explicit Cross-View Temporal Prediction for Unsupervised Learning in Spiking Neural Networks
Yiting Dong, Jianhao Ding, Zijie Xu +3
Spiking Neural Networks (SNNs), with their temporal processing capabilities and biologically plausible dynamics, offer a natural platform for unsupervised representation learning.…
CaRe-BN: Precise Moving Statistics for Stabilizing Spiking Neural Networks in Reinforcement Learning
Zijie Xu, Xinyu Shi, Yiting Dong +2
Spiking Neural Networks (SNNs) offer low-latency and energy-efficient decision-making on neuromorphic hardware by mimicking the event-driven dynamics of biological neurons. However…
Training Deep Normalization-Free Spiking Neural Networks with Lateral Inhibition
Peiyu Liu, Jianhao Ding, Zhaofei Yu
Spiking Neural Networks (SNNs) have garnered significant attention as a central paradigm in neuromorphic computing, owing to their energy efficiency and biological plausibility. Ho…
Proxy Target: Bridging the Gap Between Discrete Spiking Neural Networks and Continuous Control
Zijie Xu, Tong Bu, Zecheng Hao +2
Spiking Neural Networks (SNNs) offer low-latency and energy-efficient decision making on neuromorphic hardware, making them attractive for Reinforcement Learning (RL) in resource-c…
Differential Coding for Training-Free ANN-to-SNN Conversion
Zihan Huang, Wei Fang, Tong Bu +6
Spiking Neural Networks (SNNs) exhibit significant potential due to their low energy consumption. Converting Artificial Neural Networks (ANNs) to SNNs is an efficient way to achiev…
Towards High-performance Spiking Transformers from ANN to SNN Conversion
Zihan Huang, Xinyu Shi, Zecheng Hao +4
Spiking neural networks (SNNs) show great potential due to their energy efficiency, fast processing capabilities, and robustness. There are two main approaches to constructing SNNs…
Faster and Stronger: When ANN-SNN Conversion Meets Parallel Spiking Calculation
Zecheng Hao, Qichao Ma, Kang Chen +3
Spiking Neural Network (SNN), as a brain-inspired and energy-efficient network, is currently facing the pivotal challenge of exploring a suitable and efficient learning framework.…
Rethinking SNN Online Training and Deployment: Gradient-Coherent Learning via Hybrid-Driven LIF Model
Zecheng Hao, Yifan Huang, Zijie Xu +4
Spiking Neural Networks (SNNs) are considered to have enormous potential in the future development of Artificial Intelligence due to their brain-inspired and energy-efficient prope…
Inference-Scale Complexity in ANN-SNN Conversion for High-Performance and Low-Power Applications
Tong Bu, Maohua Li, Zhaofei Yu
Spiking Neural Networks (SNNs) have emerged as a promising substitute for Artificial Neural Networks (ANNs) due to their advantages of fast inference and low power consumption. How…
SpikingResformer: Bridging ResNet and Vision Transformer in Spiking Neural Networks
Xinyu Shi, Zecheng Hao, Zhaofei Yu
The remarkable success of Vision Transformers in Artificial Neural Networks (ANNs) has led to a growing interest in incorporating the self-attention mechanism and transformer-based…