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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}
}

@inproceedings{wang2026circuitnet3,
    title={CircuitNet 3.0: A Multi-Modal Dataset with Task-Oriented Augmentation for AI-Driven Circuit Design},
    author={Wang, Mingjun and Wen, Yihan and Lu, Yuntao and Liu, Fengrui and Zhao, Yuxiang and Han, Boyu and Mu, Jianan and Lin, Yibo and Wang, Runsheng and Yu, Bei and Li, Huawei},
    booktitle={The Fourteenth International Conference on Learning Representations},
    year={2026}
}