35 citations · 80 across the 23 of their papers we have counts for
25 papers
Neuromorphic Object Detection: An In-Depth Study and Future Directions
Jianing Li, Dianze Li, Arren Glover +5
Conventional frame-based cameras face significant challenges in detecting objects under high-speed motion blur or in low-light environments. Neuromorphic cameras provide asynchrono…
FSD-VLN: Fast-Slow Dual-System Modeling for Aerial Long-Horizon Vision-Language Navigation
Xueke Zhu, Qingyan Meng, Liutao Yu +4
Vision-Language Navigation (VLN) enables UAV autonomous navigation in unknown environments by mapping language instructions to real-time visual inputs. Compared with GPS-dependent…
Burst Spiking Neural Networks
Jiahong Zhang, Sijun Shen, Man Yao +5
A central goal of current Spiking Neural Network (SNN) research is to improve their accuracy toward becoming low-power alternatives to Artificial Neural Networks (ANNs). This work…
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…
Hierarchical Zero-Order Optimization for Deep Neural Networks
Sansheng Cao, Zhengyu Ma, Yonghong Tian
Zeroth-order (ZO) optimization has long been favored for its biological plausibility and its capacity to handle non-differentiable objectives, yet its computational complexity has…
A Self-Ensemble Inspired Approach for Effective Training of Binary-Weight Spiking Neural Networks
Qingyan Meng, Mingqing Xiao, Zhengyu Ma +3
Spiking Neural Networks (SNNs) are a promising approach to low-power applications on neuromorphic hardware due to their energy efficiency. However, training SNNs is challenging bec…
Multiplication-Free Parallelizable Spiking Neurons with Efficient Spatio-Temporal Dynamics
Peng Xue, Wei Fang, Zhengyu Ma +5
Spiking Neural Networks (SNNs) are distinguished from Artificial Neural Networks (ANNs) for their complex neuronal dynamics and sparse binary activations (spikes) inspired by the b…
A High Energy-Efficiency Multi-core Neuromorphic Architecture for Deep SNN Training
Mingjing Li, Huihui Zhou, Xiaofeng Xu +14
There is a growing necessity for edge training to adapt to dynamically changing environment. Neuromorphic computing represents a significant pathway for high-efficiency intelligent…
Scalable Dendritic Modeling Advances Expressive and Robust Deep Spiking Neural Networks
Yifan Huang, Wei Fang, Zhengyu Ma +2
Dendritic computation endows biological neurons with rich nonlinear integration and high representational capacity, yet it is largely missing in existing deep spiking neural networ…
HDI-Former: Hybrid Dynamic Interaction ANN-SNN Transformer for Object Detection Using Frames and Events
Dianze Li, Jianing Li, Xu Liu +3
Combining the complementary benefits of frames and events has been widely used for object detection in challenging scenarios. However, most object detection methods use two indepen…
Core Placement Optimization of Many-core Brain-Inspired Near-Storage Systems for Spiking Neural Network Training
Xueke Zhu, Wenjie Lin, Yanyu Lin +5
With the increasing application scope of spiking neural networks (SNN), the complexity of SNN models has surged, leading to an exponential growth in demand for AI computility. As t…
Spatial-Temporal Search for Spiking Neural Networks
Kaiwei Che, Zhaokun Zhou, Li Yuan +3
Spiking Neural Networks (SNNs) are considered as a potential candidate for the next generation of artificial intelligence with appealing characteristics such as sparse computation…
Efficiently Training Time-to-First-Spike Spiking Neural Networks from Scratch
Kaiwei Che, Zhengyu Ma, Yifan Huang +5
Spiking Neural Networks (SNNs), with their event-driven and biologically inspired mechanisms, are well-suited for energy-efficient neuromorphic hardware. Neural coding, which is cr…
Time-Evolving Dynamical System for Learning Latent Representations of Mouse Visual Neural Activity
Liwei Huang, ZhengYu Ma, Liutao Yu +2
Seeking high-quality representations with latent variable models (LVMs) to reveal the intrinsic correlation between neural activity and behavior or sensory stimuli has attracted mu…
SVFormer: A Direct Training Spiking Transformer for Efficient Video Action Recognition
Liutao Yu, Liwei Huang, Chenlin Zhou +4
Video action recognition (VAR) plays crucial roles in various domains such as surveillance, healthcare, and industrial automation, making it highly significant for the society. Con…
High-Performance Temporal Reversible Spiking Neural Networks with Training Memory and Inference Cost
JiaKui Hu, Man Yao, Xuerui Qiu +6
Multi-timestep simulation of brain-inspired Spiking Neural Networks (SNNs) boost memory requirements during training and increase inference energy cost. Current training methods ca…
Direct Training High-Performance Deep Spiking Neural Networks: A Review of Theories and Methods
Chenlin Zhou, Han Zhang, Liutao Yu +7
Spiking neural networks (SNNs) offer a promising energy-efficient alternative to artificial neural networks (ANNs), in virtue of their high biological plausibility, rich spatial-te…
Spike-driven Transformer V2: Meta Spiking Neural Network Architecture Inspiring the Design of Next-generation Neuromorphic Chips
Man Yao, Jiakui Hu, Tianxiang Hu +5
Neuromorphic computing, which exploits Spiking Neural Networks (SNNs) on neuromorphic chips, is a promising energy-efficient alternative to traditional AI. CNN-based SNNs are the c…
QKFormer: Hierarchical Spiking Transformer using Q-K Attention
Chenlin Zhou, Han Zhang, Zhaokun Zhou +7
Spiking Transformers, which integrate Spiking Neural Networks (SNNs) with Transformer architectures, have attracted significant attention due to their potential for energy efficien…
Optimal ANN-SNN Conversion with Group Neurons
Liuzhenghao Lv, Wei Fang, Li Yuan +1
Spiking Neural Networks (SNNs) have emerged as a promising third generation of neural networks, offering unique characteristics such as binary outputs, high sparsity, and biologica…
Deep peak property learning for efficient chiral molecules ECD spectra prediction
Hao Li, Da Long, Li Yuan +3
Chiral molecule assignation is crucial for asymmetric catalysis, functional materials, and the drug industry. The conventional approach requires theoretical calculations of electro…
Spikformer V2: Join the High Accuracy Club on ImageNet with an SNN Ticket
Zhaokun Zhou, Yijie Lu, Kaiwei Che +7
Spiking Neural Networks (SNNs), known for their biologically plausible architecture, face the challenge of limited performance. The self-attention mechanism, which is the cornersto…
Machine Mindset: An MBTI Exploration of Large Language Models
Jiaxi Cui, Liuzhenghao Lv, Jing Wen +4
We present a novel approach for integrating Myers-Briggs Type Indicator (MBTI) personality traits into large language models (LLMs), addressing the challenges of personality consis…