Author

Shaogang Hu

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#edge computing Nov 2026

Unifying Feed-Forward and Weight-Gradient: An Energy-Efficient Architecture for On-Chip SNN Learning

Spiking neural networks (SNNs) enable efficient on-chip learning but face a critical hardware challenge due to the computational asymmetry between small-kernel convolution operations in the feed-forward (FF) stage and large-kernel ones in the weight-gradient (WG) stage. Directly mapping these conflicting patterns onto...

Yue Zuo, N. Ning, Zhi-Qiang Su et al. · IEEE Transactions on Very La... · 0 citations

Preprint Oct 2026

The Dormant Spiking Neuron: A State-Driven Mechanism for Efficient Spiking Neural Networks

Spiking neural networks (SNNs) offer a compelling pathway toward energy-efficient artificial intelligence, fundamentally anchored in presynaptic input-driven sparsity. However, a substantial yet chronically overlooked source of computational overhead persists: the continuous subthreshold dynamics of postsynaptic neuron...

Rui-Chen Ma, Yi-Cai Chen, Pu-Jun Zhou et al. · 0 citations

Jun 2026

Intrinsically Stable Spiking Neural Networks: Overcoming the Performance Barrier in the Absence of Batch Normalization

The Intrinsically Stable SNN (IS-SNN) architecture is proposed, which removes activation-normalization layers by enforcing signal homeostasis through topology-aware weight standardization and modified residual connections and removes the runtime statistics tracking and multiplications introduced by activation normaliza...

R. Ma, Xiaoyang Zhang, J. Bai et al. · arXiv.org · 0 citations