CircuitNet

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

CircuitNet is an open-source dataset dedicated to machine learning (ML) applications in electronic design automation (EDA). We have collected more than 10K samples from versatile runs of commercial design tools based on open-source RISC-V designs with various features for multiple ML for EDA applications.

This documentation is organized as followed:

• Introduction: introduction and quick start.
• Feature Description: name conventions, calculation method, characteristics and visualization.

The codes in the tutorial page is available in our github repository https://github.com/circuitnet/CircuitNet.

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.

Citation

Paper Link

@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}
}

Change Log

• 2022/8/1

  First release.

• 2022/9/6

  Pretrained weights are available in Google Drive and Baidu Netdisk.

• 2022/12/12

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

• 2022/12/29

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

• 2022/3/22

  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.