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 followed:

The codes in the tutorial page is 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.

We call for contributions from the community to expand the dataset. Please contact if you want to contribute. We will acknowledge your contributions on the website.




  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}, 
  title={CircuitNet: An Open-Source Dataset for Machine Learning in VLSI CAD Applications with Improved Domain-Specific Evaluation Metric and Learning Strategies}, 


title={CircuitNet 2.0: An Advanced Dataset for Promoting Machine Learning Innovations in Realistic Chip Design Environment},
author={Xun, Jiang and Chai, Zhuomin and Zhao, Yuxiang and Lin, Yibo and Wang, Runsheng and Huang, Ru},
booktitle={The Twelfth International Conference on Learning Representations},

Change Log

  • 2023/7/24

    Code for feature extraction released. Users can use it to implement self-defined features with the LEF/DEF we released or extract features with LEF/DEF from other sources. Read the README for more information.

  • 2023/6/29

    Code for net delay prediction released. A simple tutorial on net delay prediction is added to our website.

  • 2023/6/14

    The original dataset is renamed to CircuitNet-N28, and additional timing features are released.

    New dataset CircuitNet-N14 is released, supporting congestion, IR drop and timing prediction.

  • 2023/3/22

    LEF/DEF is updated to include tech information (sanitized).

    Congestion features and graph features generated from ISPD2015 benchmark are available in the ISPD2015 dir in Google Drive and Baidu Netdisk.

  • 2022/12/29

    LEF/DEF (sanitized) are available in the LEF&DEF dir in Google Drive and Baidu Netdisk.

  • 2022/12/12

    Graph features are available in the graph_features dir in Google Drive and Baidu Netdisk.

  • 2022/9/6

    Pretrained weights are available in Google Drive and Baidu Netdisk.

  • 2022/8/1

    First release.

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