MIMIC

Dataset of synthetic timing cones across multiple synthetic netlists for timing analysis

Requirements

• Python 3.7 or higher
• PyTorch
• PyTorch Geometric
• NumPy
• scikit-learn

You can install dependencies with:

pip install -r requirements.txt

Installation

1. Clone the repository:

   git clone https://github.com/ASU-VDA-Lab/MIMIC.git
   cd MIMIC

2. (Optional) Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate   # Linux/macOS
   venv\Scripts\activate      # Windows

3. Install Python dependencies:

   pip install -r requirements.txt

Usage

Run the generator via the src.main module. Available options:

• --num-nodes (-n): Number of nodes to condition on (default: 2000).
• --clock-period (-c): Clock period to condition on (default: 120).

# Example: generate DAGs for 1500 nodes and clock period 200
python -m src.main --num-nodes 1500 --clock-period 200

The script will:

1. Load model data and precomputed transition biases.
2. Load trained models to device (CPU or GPU).
3. Sample layer-size sequences from the VAE.
4. Construct and print summaries of each generated graph.

Output

For each sample, you'll see:

Graph #1 Summary:
  Number of nodes: <n>
  Number of edges: <e>
  Layer sizes: [ ... ]

License

MIT License. See LICENSE for details.