DuST-IRdrop

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# DuST: Fast Dynamic IR-Drop Prediction with Dual-path Spatial-Temporal Attention

This is the implementation of the [DATE'25 paper: Fast Dynamic IR-Drop Prediction with Dual-path Spatial-Temporal Attention](https://ieeexplore.ieee.org/document/10992730)

The model architecture is as shown in the following figure.

## Requirements

Dependencies are listed in `requirements.txt` and can be installed by:

```sh
pip install -r requirements.txt
```

## Dataset

Please refer to [CircuitNet](https://github.com/circuitnet/CircuitNet) to download and extract the dataset for dynamic ir drop prediction task.
Put the dataset in the `train_data/` folder. The directory structure should be like this:
```bash
├── train_data
│   ├── feature
│   │   ├── <feature_map_1>.npy
│   │   ├── <feature_map_2>.npy
│   │   ├── ...
│   ├── label
│   │   ├── <label_map_1>.npy
│   │   ├── <label_map_2>.npy
│   │   ├── ...
```
The dataset is indexed by the files under `index/` folder.

## Configuration
Please refer to `utils/configs.py` to modify the configurations, where the default parameters are used in our experiments.

## Usage
The pretrained model can be downloaded [here](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155168650_link_cuhk_edu_hk/EZBZToSyUSBOnHOZ_KuI5yUBlm4MudAcQQQFOMoh_H7bSQ?e=x2CwOn).
We provide a pretrained model in `pretrained/model_iters_176950.pth` and can be downloaded by:
```bash
cd pretrained
bash download.sh
```

Please refer to `train.py` to perform the training. The testing is included in `test.py`. 
```bash
# for Training
python train.py --model_type IRNetDual

# for Testing
python test.py --model_type IRNetDual --pretrained pretrained/model_iters_176950.pth --result_dir results/test
```

## Citation
If you find our work useful in your research, please consider to cite:
```bibtex
@inproceedings{fu2025ir_predict,
    author={Fu, Bangqi and Liu, Lixin and Wang, Qijing and Wang, Yutao and Wong, Martin D. F. and Young, Evangeline F. Y.},
    booktitle={Proceedings of the 2025 IEEE/ACM Design, Automation and Test in Europe Conference},
    title={Fast Dynamic IR-Drop Prediction with Dual-path Spatial-Temporal Attention},
    year={2025},
}

```

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