PD_WM_GNN

Artifact evaluation for MLCAD 2024 paper "Automated Physical Design Watermarking Leveraging Graph Neural Networks"

Environment Setup

# clone code
git clone --recursive https://github.com/ruisizhang123/PD_WM_GNN.git
cd PD_WM_GNN
# create conda env
conda create -n  pd_wm_gnn python=3.9
conda activate pd_wm_gnn
# install required packages
pip install -r requirement.txt
# build
bash build.sh

Our codebase builds heavily upon DREAMPlace. If the environment missed some packages, you can alternatively (1) look for instructions from DREAMPlace repo or (2) build with dock file as follows:

docker build . --file Dockerfile --tag PD_WM_GNN/dreamplace:cuda

Watermark layout

1. Download required benchmarks

cd benchmarks
python ispd2005_2015.py
python ispd2019.py

2. Watermark design (Inference)

Watermark ISPD19 test1 design, with our pre-trained GNN model.

python dreamplace/Placer.py test/ispd2019/lefdef/ispd19_test1.json ./test/graph.json 

We use CU-GR to route the wm'ed layout. Alternatively, you can use pre-built binary software to evaluate:

./iccad19gr_upd -lef  benchmarks/ispd2019/ispd19_test1/ispd19_test1.input.lef -def  results/ispd19_test1.input/ispd19_test1.input.500.def -output result.solution.guide -threads 8  >> results/ispd19_test1.input/ispd19_test1/log.txt

3. Train GNN from scratch

Change mode in ./test/graph.json from inference to train. We collect and train our model on ISPD19 test6 design.

python dreamplace/Placer.py test/ispd2019/lefdef/ispd19_test6.json ./test/graph.json 

4. Attack Evaluation

Change attack in ./test/graph.json from 0 to 1.

python dreamplace/Placer.py test/ispd2019/lefdef/ispd19_test1.json ./test/graph.json 

Citation

If you found our code/paper helpful, please kindly cite:

@inproceedings{zhang2024automated,
  title={Automated Physical Design Watermarking Leveraging Graph Neural Networks},
  author={Zhang, Ruisi and Rajarathnam, Rachel Selina and Pan, David Z and Koushanfar, Farinaz},
  booktitle={Proceedings of the 2024 ACM/IEEE International Symposium on Machine Learning for CAD},
  pages={1--10},
  year={2024}
}