Download

Choose the appropriate dataset based on your target tasks:

Available Datasets

CircuitNet-N28

• Google Drive
• Baidu Netdisk

CircuitNet-N14

• 🤗 Hugging Face

Decompress and Preprocess

Routability/IR Drop Features

Step 1: Decompress the Dataset

Choose one of the following commands based on your needs:

# For Routability features
python decompress_routability.py

# For IR Drop features
python decompress_IR_drop.py

Step 2: Generate Training Set

Run the preprocessing script to generate the training set for your specific task:

python generate_training_set.py \
    --task [congestion/DRC/IR_drop] \
    --data_path [path_to_decompressed_dataset] \
    --save_path [path_to_save_output]

Step 3: Start Training

You can now:

• Set up your own model for training
• Use our tutorial code from the tutorial page
• Check our GitHub repository for implementation details

Graph Features

Step 1: Decompress the Dataset

tar -xf PATH_TO_THE_FILE

Step 2: Construct Graph

Sample code for graph construction will be available soon.

Raw Data

We provide the following raw data formats (CircuitNet-N28) for custom feature extraction:

• Netlist files
• LEF/DEF files

Raw data for CircuitNet-N14 is coming soon. Currently available upon request.

Feature Extraction

You can use our feature extraction toolkit to:

• Extract custom features from raw data
• Process and transform the data for your specific needs
• Integrate with your own machine learning pipeline

For detailed instructions on feature extraction, please refer to our feature documentation.