# NetlistGNN

**Repository Path**: kiloGrand/NetlistGNN

## Basic Information

- **Project Name**: NetlistGNN
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-03-29
- **Last Updated**: 2025-03-29

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# NetlistGNN

Code for paper [Versatile Multi-stage Graph Neural Network for Circuit Representation](https://openreview.net/forum?id=nax3ATLrovW) in NeurIPS 2022

## For simple test

Note that **NetlistGNN** is the former name of our model, now it's revised to **Circuit GNN**, but some parts of the code are not revised.

The download links of ISPD2011 and DAC2012 are listed in the paper.

Preprocessed by [DREAMPlace](https://github.com/limbo018/DREAMPlace.git)

As the raw data are too big, they are not included here.

Run following command to train the **Circuit GNN** on `superblue19`:
```commandline
python script_train_sample.py
```

## For reproduction

1. Get and store the output data of DREAMPlace in `{data_dir}`.
2. Config and run `data/script_process.py` to get processed data (in `{data_dir}-processed`).
3. Config and run `script_train.py` to train **CircuitGNN**:
   1. Beyond the args, `train_dataset_names`, `validate_dataset_name` and `test_dataset_name` should also be configured.
   2. At the first time of training, a Circuit Graph will be generated in `{data_dir}-processed/hetero_{given_iter}`. This might cost about an hour.

## For industrial use

Collect the data.

Configurate `train_dataset_names`, `validate_dataset_name` and `test_dataset_name` in `script_ours_cong_train.py` and train model:
```commandline
python script_ours_cong_train.py --name trained_model
```

Configurate `eval_dataset_names` in `script_ours_cong_eval.py` and evaluate model:
```commandline
python script_ours_cong_eval.py --name eval --model trained_model
```