sysu2023 数据集：https://github.com/TimingPredict/Dataset 原仓库README如下
This is the official open source repository for "A Timing Engine Inspired Graph Neural Network Model for Pre-Routing Slack Prediction" (DAC 2022).
Please refer to our Dataset repo for download instructions and documentation.
First, download the dataset and extract to <repo root>/data/.
Then, download our pre-trained weight here or here or here and put it under <repo root>/checkpoints/08_atcd_specul/15799.pth. (Note that Google drive is showing it as an archive but please download it as a whole.)
Finally, run the following command:
python train_gnn.py --test_iter 15799 --checkpoint 08_atcd_speculThis prints two tables indicating the net delay and arrival time prediction results, same as that in our paper.
data_graph.py: the data reader and pre-processor.
model.py: our customized GNN model implementation (please refer to the class NetConv, SignalProp, and TimingGCN). also inside it is our GCNII baseline implementation.
train_gnn.py: the model training and testing logic.
train_stat_rf.py, train_deepgnn.py: model training and testing for random forest and deep GCNII baselines.
Please cite our work if you find our code useful.
@inproceedings{mltimerdac22,
author = {Guo, Zizheng and Liu, Mingjie and Gu, Jiaqi and Zhang, Shuhan and Pan, David Z. and Lin, Yibo},
booktitle = {Proceedings of the 59th Annual Design Automation Conference 2022},
organization = {ACM},
title = {A Timing Engine Inspired Graph Neural Network Model for Pre-Routing Slack Prediction},
year = {2022}
}