linboqiao / CircuitNet

CircuitNet: An Open-Source Dataset for Machine Learning Applications in Electronic Design Automation (EDA)

CircuitNet: An Open-Source Dataset for Machine Learning Applications in Electronic Design Automation (EDA)

Overview

This repository is intended to hosts codes and demos for CircuitNet, we hope this codebase would be helpful for users to reproduce exiting methods. More information about the dataset can be accessed from our web page https://circuitnet.github.io/.

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New Features/Updates

• Sep 6, 2022. Pretrained weights are available in checkpoint dir.

Prerequisites

Dependencies can be installed using pip:

pip install -r requirements.txt

PyTorch is not included in requirement.txt, and you could intall it follow the instruction in PyTorch homepage https://pytorch.org/.

Our experiments run on Python 3.9 and PyTorch 1.11. Other versions should work but are not tested.

Data Preparation

Please follow the instructions in the quick start page to setup the CircuitNet dataset for specific task(Congestion/DRC/IR Drop).

Example Usage:

Change the configure in utils/config.py to fit your file path and adjust hyper-parameter before starting.

Test

Congestion

python test.py --task congestion_gpdl --pretrained PRETRAINED_WEIGHTS_PATH

DRC

python test.py --task drc_routenet --pretrained PRETRAINED_WEIGHTS_PATH --save_as_npy

IR Drop

python test.py --task irdrop_mavi --pretrained PRETRAINED_WEIGHTS_PATH --save_as_npy

Train

Congestion

python train.py --task congestion_gpdl --save_path work_dir/congestion_gpdl/

DRC

python train.py --task drc_routenet --save_path work_dir/drc_routenet/

IR Drop

python train.py --task irdrop_mavi --save_path work_dir/irdrop_mavi/

License

This repository is released under the BSD 3-Clause. license as found in the LICENSE file.

Citation

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

@article{chai2022circuitnet,
    title = {CircuitNet: An Open-Source Dataset for Machine Learning Applications in Electronic Design Automation (EDA)},
    author = {Chai, Zhuomin and Zhao, Yuxiang and Lin, Yibo and Liu, Wei and Wang, Runsheng and Huang, Ru},
    journal= {SCIENCE CHINA Information Sciences},
    volume={65},
    number = "12",
    pages={227401-},
    year = {2022}
}

Contact

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

Zhuomin Chai: [e-mail]
Yuxiang Zhao: [e-mail]