7 citations · 15 across the 13 of their papers we have counts for
13 papers
DarwinWafer: A Wafer-Scale Neuromorphic Chip
Xiaolei Zhu, Xiaofei Jin, Ziyang Kang +11
Neuromorphic computing promises brain-like efficiency, yet today's multi-chip systems scale over PCBs and incur orders-of-magnitude penalties in bandwidth, latency, and energy, und…
Spiking Neural Networks with Temporal Attention-Guided Adaptive Fusion for imbalanced Multi-modal Learning
Jiangrong Shen, Yulin Xie, Qi Xu +3
Multimodal spiking neural networks (SNNs) hold significant potential for energy-efficient sensory processing but face critical challenges in modality imbalance and temporal misalig…
Hybrid Spiking Vision Transformer for Object Detection with Event Cameras
Qi Xu, Jie Deng, Jiangrong Shen +3
Event-based object detection has gained increasing attention due to its advantages such as high temporal resolution, wide dynamic range, and asynchronous address-event representati…
Darkit: A User-Friendly Software Toolkit for Spiking Large Language Model
Xin Du, Shifan Ye, Qian Zheng +7
Large language models (LLMs) have been widely applied in various practical applications, typically comprising billions of parameters, with inference processes requiring substantial…
ALADE-SNN: Adaptive Logit Alignment in Dynamically Expandable Spiking Neural Networks for Class Incremental Learning
Wenyao Ni, Jiangrong Shen, Qi Xu +1
Inspired by the human brain's ability to adapt to new tasks without erasing prior knowledge, we develop spiking neural networks (SNNs) with dynamic structures for Class Incremental…
Enhancing SNN-based Spatio-Temporal Learning: A Benchmark Dataset and Cross-Modality Attention Model
Shibo Zhou, Bo Yang, Mengwen Yuan +4
Spiking Neural Networks (SNNs), renowned for their low power consumption, brain-inspired architecture, and spatio-temporal representation capabilities, have garnered considerable a…
Spiking GS: Towards High-Accuracy and Low-Cost Surface Reconstruction via Spiking Neuron-based Gaussian Splatting
Weixing Zhang, Zongrui Li, De Ma +4
3D Gaussian Splatting is capable of reconstructing 3D scenes in minutes. Despite recent advances in improving surface reconstruction accuracy, the reconstructed results still exhib…
Toward Large-scale Spiking Neural Networks: A Comprehensive Survey and Future Directions
Yangfan Hu, Qian Zheng, Guoqi Li +2
Deep learning has revolutionized artificial intelligence (AI), achieving remarkable progress in fields such as computer vision, speech recognition, and natural language processing.…
Context Gating in Spiking Neural Networks: Achieving Lifelong Learning through Integration of Local and Global Plasticity
Jiangrong Shen, Wenyao Ni, Qi Xu +2
Humans learn multiple tasks in succession with minimal mutual interference, through the context gating mechanism in the prefrontal cortex (PFC). The brain-inspired models of spikin…
Enhancing Adaptive History Reserving by Spiking Convolutional Block Attention Module in Recurrent Neural Networks
Qi Xu, Yuyuan Gao, Jiangrong Shen +4
Spiking neural networks (SNNs) serve as one type of efficient model to process spatio-temporal patterns in time series, such as the Address-Event Representation data collected from…
Darwin3: A large-scale neuromorphic chip with a Novel ISA and On-Chip Learning
De Ma, Xiaofei Jin, Shichun Sun +8
Spiking Neural Networks (SNNs) are gaining increasing attention for their biological plausibility and potential for improved computational efficiency. To match the high spatial-tem…
Mitigating Communication Costs in Neural Networks: The Role of Dendritic Nonlinearity
Xundong Wu, Pengfei Zhao, Zilin Yu +6
Our understanding of biological neuronal networks has profoundly influenced the development of artificial neural networks (ANNs). However, neurons utilized in ANNs differ considera…
SPAIC: A Spike-based Artificial Intelligence Computing Framework
Chaofei Hong, Mengwen Yuan, Mengxiao Zhang +6
Neuromorphic computing is an emerging research field that aims to develop new intelligent systems by integrating theories and technologies from multi-disciplines such as neuroscien…