most citedToward Large-scale Spiking Neural Networks: A Comprehensive Survey and Future Directions

3 citations · 4 across the 6 of their papers we have counts for

collaborators

8 papers

cs.ET2025

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…

cs.LG20251 cited

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…

cs.CV2025

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…

cs.SE2024

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…

cs.CV2024

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…

cs.CV2024

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…