7 papers
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…
BrainFuse: a unified infrastructure integrating realistic biological modeling and core AI methodology
Baiyu Chen, Yujie Wu, Siyuan Xu +9
Neuroscience and artificial intelligence represent distinct yet complementary pathways to general intelligence. However, amid the ongoing boom in AI research and applications, the…
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…