NeuronQuant: Accurate and Efficient Post-Training Quantization for Spiking Neural Networks

Haomin Li*, Fangxin Liu*, Zewen Sun, Zongwu Wang, Shiyuan Huang, Ning Yang, Li Jiang

This is the official implementation of the paper "NeuronQuant: Accurate and Efficient Post-Training Quantization for Spiking Neural Networks" [ASP-DAC 2025]

Introduction

Prepare Models and Running Quantization

./run.sh

Citation

@inproceedings{li2025neuronquant,
  title={NeuronQuant: Accurate and Efficient Post-Training Quantization for Spiking Neural Networks},
  author={Li, Haomin and Liu, Fangxin and Sun, Zewen and Wang, Zongwu and Huang, Shiyuan and Yang, Ning and Jiang, Li},
  booktitle={2025 30th Asia and South Pacific Design Automation Conference (ASP-DAC)},
  year={2025}
}

Acknowledgement

Our Code is based on the implementation of ANN2SNN_SRP.

Contact Us

If you have any questions, please contact:

• Haomin Li: haominli@sjtu.edu.cn
• Fangxin Liu: liufangxin@sjtu.edu.cn
• Zewen Sun: 3022244294@tju.edu.cn
• Li Jiang: ljiang_cs@sjtu.edu.cn