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.




  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 REAME 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.

results matching ""

    No results matching ""