88 citations · 123 across the 22 of their papers we have counts for
23 papers
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…
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…
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.…
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…
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…
Towards Low-latency Event-based Visual Recognition with Hybrid Step-wise Distillation Spiking Neural Networks
Xian Zhong, Shengwang Hu, Wenxuan Liu +4
Spiking neural networks (SNNs) have garnered significant attention for their low power consumption and high biological interpretability. Their rich spatio-temporal information proc…
Robust Stable Spiking Neural Networks
Jianhao Ding, Zhiyu Pan, Yujia Liu +2
Spiking neural networks (SNNs) are gaining popularity in deep learning due to their low energy budget on neuromorphic hardware. However, they still face challenges in lacking suffi…
Enhancing Adversarial Robustness in SNNs with Sparse Gradients
Yujia Liu, Tong Bu, Jianhao Ding +3
Spiking Neural Networks (SNNs) have attracted great attention for their energy-efficient operations and biologically inspired structures, offering potential advantages over Artific…
Converting High-Performance and Low-Latency SNNs through Explicit Modelling of Residual Error in ANNs
Zhipeng Huang, Jianhao Ding, Zhiyu Pan +4
Spiking neural networks (SNNs) have garnered interest due to their energy efficiency and superior effectiveness on neuromorphic chips compared with traditional artificial neural ne…
Defense Against Adversarial Attacks on No-Reference Image Quality Models with Gradient Norm Regularization
Yujia Liu, Chenxi Yang, Dingquan Li +2
The task of No-Reference Image Quality Assessment (NR-IQA) is to estimate the quality score of an input image without additional information. NR-IQA models play a crucial role in t…
SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence
Wei Fang, Yanqi Chen, Jianhao Ding +7
Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic chips with high energy efficiency by introducing neural dynamics and spike properties. As…
Spike timing reshapes robustness against attacks in spiking neural networks
Jianhao Ding, Zhaofei Yu, Tiejun Huang +1
The success of deep learning in the past decade is partially shrouded in the shadow of adversarial attacks. In contrast, the brain is far more robust at complex cognitive tasks. Ut…
Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks
Tong Bu, Wei Fang, Jianhao Ding +3
Spiking Neural Networks (SNNs) have gained great attraction due to their distinctive properties of low power consumption and fast inference on neuromorphic hardware. As the most ef…
SpikeCV: Open a Continuous Computer Vision Era
Yajing Zheng, Jiyuan Zhang, Rui Zhao +5
SpikeCV is a new open-source computer vision platform for the spike camera, which is a neuromorphic visual sensor that has developed rapidly in recent years. In the spike camera, e…
Bridging the Gap between ANNs and SNNs by Calibrating Offset Spikes
Zecheng Hao, Jianhao Ding, Tong Bu +2
Spiking Neural Networks (SNNs) have attracted great attention due to their distinctive characteristics of low power consumption and temporal information processing. ANN-SNN convers…
Reducing ANN-SNN Conversion Error through Residual Membrane Potential
Zecheng Hao, Tong Bu, Jianhao Ding +2
Spiking Neural Networks (SNNs) have received extensive academic attention due to the unique properties of low power consumption and high-speed computing on neuromorphic chips. Amon…
Optimized Potential Initialization for Low-latency Spiking Neural Networks
Tong Bu, Jianhao Ding, Zhaofei Yu +1
Spiking Neural Networks (SNNs) have been attached great importance due to the distinctive properties of low power consumption, biological plausibility, and adversarial robustness.…
Optimal ANN-SNN Conversion for Fast and Accurate Inference in Deep Spiking Neural Networks
Jianhao Ding, Zhaofei Yu, Yonghong Tian +1
Spiking Neural Networks (SNNs), as bio-inspired energy-efficient neural networks, have attracted great attentions from researchers and industry. The most efficient way to train dee…
A Fixed point view: A Model-Based Clustering Framework
Jianhao Ding, Lansheng Han
With the inflation of the data, clustering analysis, as a branch of unsupervised learning, lacks unified understanding and application of its mathematical law. Based on the view of…