If you use ANT or OliVe in your research, please cite our paper:
@inproceedings{guo2022ant,
title={ANT: Exploiting Adaptive Numerical Data Type for Low-bit Deep Neural Network Quantization},
author={Guo, Cong and Zhang, Chen and Leng, Jingwen and Liu, Zihan and Yang, Fan and Liu, Yunxin and Guo, Minyi and Zhu, Yuhao},
booktitle={2022 55th IEEE/ACM International Symposium on Microarchitecture (MICRO)},
pages={1414--1433},
year={2022},
organization={IEEE}
}@article{guo2023olive,
author = {Guo, Cong and Tang, Jiaming and Hu, Weiming and Leng, Jingwen and Zhang, Chen and Yang, Fan and Liu, Yunxin and Guo, Minyi and Zhu, Yuhao},
title = {OliVe: Accelerating Large Language Models via Hardware-friendly Outlier-Victim Pair Quantization},
year = {2023},
eprint = {arXiv:2304.07493},
doi = {10.1145/3579371.3589038},
}This repository contains the source code for reproducing the experiments in the paper "ANT: Exploiting Adaptive Numerical Data Type for Low-bit Deep Neural Network Quantization" at MICRO'22 and "OliVe: Accelerating Large Language Models via Hardware-friendly Outlier-Victim Pair Quantization" at ISCA'23.
ant_quantization contains the ANT framework with PyTorch.
olive_quantization contains the OliVe framework with PyTorch.
ant_simulator contains the performance and energy evaluation of ANT.
Licensed under an Apache-2.0 license.