Jiaqi Yan — accepted AI-conference papers

2025

Training High Performance Spiking Neural Network by Temporal Model Calibration

Jiaqi Yan, Changping Wang, De Ma, Huajin Tang, Qian Zheng, Gang Pan

ICML 2025poster

Spiking Neural Networks (SNNs) are considered promising energy-efficient models due to their dynamic capability to process spatial-temporal spike information. Existing work has demonstrated that SNNs exhibit temporal heterogeneity, which leads to diverse outputs of SNNs at different time steps and h…

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2024

LitE-SNN: Designing Lightweight and Efficient Spiking Neural Network through Spatial-Temporal Compressive Network Search and Joint Optimization

Qianhui Liu, Jiaqi Yan, Malu Zhang, Gang Pan, Haizhou Li

IJCAI 2024poster

Spiking Neural Networks (SNNs) mimic the information-processing mechanisms of the human brain and are highly energy-efficient, making them well-suited for low-power edge devices. However, the pursuit of accuracy in current studies leads to large, long-timestep SNNs, conflicting with the resource con…

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