MacroRank: Ranking Macro Placement Solutions Leveraging Translation Equivariancy

Overview

Official implementation of our MacroRank, which can accurately predict the relative order of the quality of macro placement solutions.

Download Data and Model

Data&Model

Requirements

conda env create -f env.yaml

Test

bash bin/test_cnn.sh
bash bin/test_hgnn.sh
bash bin/test_ehnn.sh
bash bin/test_macrorank.sh

License

This repository is released under the MIT License.

Citation

If you think our work is useful, please feel free to cite our paper.

@inproceedings{chen2023macrorank,
    author = {Chen, Yifan and Mai, Jing and Gao, Xiaohan and Zhang, Muhan and Lin, Yibo},
    title = {MacroRank: Ranking Macro Placement Solutions Leveraging Translation Equivariancy},
    booktitle = {IEEE/ACM Asia and South Pacific Design Automation Conference (ASPDAC)},
    year = {2023},
    isbn = {9781450397834},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    url = {https://doi.org/10.1145/3566097.3567899},
    doi = {10.1145/3566097.3567899},
    pages = {258–263},
    numpages = {6},
    location = {Tokyo, Japan},
}

Contact

For any questions, please do not hesitate to contact us.

Yifan Chen: chenyifan2019@pku.edu.cn

PKU-IDEA/MacroRank