NeuronQuant
[ASP-DAC 2025] "NeuronQuant: Accurate and Efficient Post-Training Quantization for Spiking Neural Networks" Official Implementation
Install / Use
/learn @shieldforever/NeuronQuantREADME
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
