CircuitNet
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:
- 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 or raise a issue in our github repository.
Citation
@ARTICLE{10158384,
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},
year={2023},
doi={10.1109/TCAD.2023.3287970}}
}
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.