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CircuitNet is an open-source dataset dedicated to machine learning (ML) applications in electronic design automation (EDA). We have collected more than 20K samples from versatile runs of commercial design tools based on open-source designs with various features for multiple ML for EDA applications.

This documentation is organized as follows:

• Dataset: introduction and quick start.
• Features: name conventions, calculation method, characteristics and visualization.
• Tutorial: tutorials for four prediction tasks with code available in our github repository.

This project is under active development. We are expanding the dataset to include diverse and large-scale designs for versatile ML applications in EDA. If you have any feedback or questions, please feel free to contact us or raise a issue in our github repository.

If you use CircuitNet for your research, please cite the following TCAD and ICLR papers.

@article{chai2023circuitnet,
  title={Circuitnet: An open-source dataset for machine learning in vlsi cad applications with improved domain-specific evaluation metric and learning strategies},
  author={Chai, Zhuomin and Zhao, Yuxiang and Liu, Wei and Lin, Yibo and Wang, Runsheng and Huang, Ru},
  journal={IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems},
  volume={42},
  number={12},
  pages={5034--5047},
  year={2023},
  publisher={IEEE}
}

@inproceedings{jiang2024circuitnet,
  title={Circuitnet 2.0: An advanced dataset for promoting machine learning innovations in realistic chip design environment},
  author={Jiang, Xun and Chai, Zhuomin and Zhao, Yuxiang and Lin, Yibo and Wang, Runsheng and Huang, Ru and others},
  booktitle={The Twelfth International Conference on Learning Representations},
  year={2024}
}