Artifact Evaluation for ANT [MICRO'22] and OliVe [ISCA'23]

Citation

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.

Project Structure

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.

License

Licensed under an Apache-2.0 license.