Xplace-NN is a neural-enhanced extension of Xplace developed by the research team supervised by Prof. Evangeline F. Y. Young at The Chinese University of Hong Kong (CUHK). Xplace-NN supports deterministic mode now.

Xplace is a fast and extensible GPU-accelerated global placement framework developed by the research team supervised by Prof. Evangeline F. Y. Young at The Chinese University of Hong Kong (CUHK). It achieves around 3x speedup per GP iteration compared to DREAMPlace and shows high extensibility.

As shown in the following figure, Xplace framework is built on top of PyTorch and consists of several independent modules. One can easily extend Xplace by applying scheduling techniques, gradient functions, new placement metrics, and so on.

More details are in the following paper:

Lixin Liu, Bangqi Fu, Martin D. F. Wong, and Evangeline F. Y. Young. "Xplace: an extremely fast and extensible global placement framework". In Proceedings of the 59th ACM/IEEE Design Automation Conference (DAC '22). Association for Computing Machinery, New York, NY, USA, 1309–1314.

(For the Xplace, please refer to branch main)

• CMake >= 3.12
• GCC >= 7.5.0
• Boost >= 1.56.0
• CUDA >= 11.3
• Python >= 3.8
• PyTorch >= 1.12.0
• Cairo
• Innovus® (version 20.14, optional, for detailed routing and design rule checking)

1. Clone the Xplace repository. We'll call the directory that you cloned Xplace as $XPLACE_HOME.

git clone --recursive https://github.com/cuhk-eda/Xplace
git checkout neural

2. Build the shared libraries used in Xplace.

cd $XPLACE_HOME
mkdir build && cd build
cmake -DPYTHON_EXECUTABLE=$(which python) ..
make -j40 && make install

The following script will automatically download ispd2005, ispd2015, and iccad2019 benchmarks in ./data/raw. It also preprocesses ispd2015 benchmark to fix some errors when routing them by Innovus®.

cd $XPLACE_HOME/data
./download_data.sh

The pre-trained FNO model is provided.

• To run GP + DP flow for ISPD2005 dataset:

# only run adaptec1
python main.py --dataset ispd2005 --design_name adaptec1 --load_from_raw True --detail_placement True

# run all the designs in ispd2005
python main.py --dataset ispd2005 --run_all True --load_from_raw True --detail_placement True

• To train FNO (optional):

python main_train_fno.py

NOTE: We default enable the deterministic mode. If you don't need determinism and want to run placement in an extremely fast mode, please try to set --deterministic False in the Python arguments.

• Each run will generate several output files in ./result/exp_id. These files can provide valuable information for parameter tuning.

In ./result/exp_id
   - eval    # parameter curves and the visualization of placement solutions
   - log     # log and statistics
   - output  # placement solutions

Please refer to main.py and main_train_fno.py.

The following script will dump the parsed design into a single torch pt file so Xplace can load the design from the pt file instead of parsing the input file from scratch.

cd $XPLACE_HOME/data
python convert_design_to_torch_data.py --dataset ispd2005

Preprocessed data is saved in ./data/cad.

To develop a new global placement technique in Xplace, we highly suggest using the pt mode to save the parser time. (set --load_from_raw False)

python main.py --dataset ispd2005 --run_all True --load_from_raw False

NOTE:

1. Please remember to use the raw mode (set --load_from_raw True) when measuring the total running time.
2. If you want to run pt mode for the custom dataset, you need to add the custom dataset path in utils/get_design_params.py.

If you find Xplace or Xplace-NN useful in your research, please consider to cite:

@inproceedings{liu2022xplace,
    author={Liu, Lixin and Fu, Bangqi and Wong, Martin D. F. and Young, Evangeline F. Y.},
    booktitle={Proceedings of the 59th ACM/IEEE Design Automation Conference},
    title={Xplace: An Extremely Fast and Extensible Global Placement Framework},
    year={2022},
}

Thanks the authors of ePlace, RePlAce, DREAMPlace, and FNO for their great work.

@article{lu2015eplace,
    author={Lu, Jingwei and Chen, Pengwen and Chang,   Chin-Chih and Sha, Lu and Huang, Dennis Jen-Hsin and   Teng, Chin-Chi and Cheng, Chung-Kuan},
    journal={ACM Trans. Des. Autom. Electron. Syst.},
    title={ePlace: Electrostatics-Based Placement Using   Fast Fourier Transform and Nesterov's Method},
    year={2015},
}

@article{cheng2019replace,
    author={Cheng, Chung-Kuan and Kahng, Andrew B. and Kang, Ilgweon and Wang, Lutong},
    journal={IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems}, 
    title={RePlAce: Advancing Solution Quality and Routability Validation in Global Placement}, 
    year={2019},
}

@article{lin2021dreamplace,
    author={Lin, Yibo and Jiang, Zixuan and Gu, Jiaqi and Li, Wuxi and Dhar, Shounak and Ren, Haoxing and Khailany, Brucek and Pan, David Z.},
    journal={IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems}, 
    title={DREAMPlace: Deep Learning Toolkit-Enabled GPU Acceleration for Modern VLSI Placement}, 
    year={2021},
}

@inproceedings{li2021fourier,
    author={Zongyi Li and Nikola Borislavov Kovachki and Kamyar Azizzadenesheli and Burigede liu and Kaushik Bhattacharya and Andrew Stuart and Anima Anandkumar},
    booktitle={International Conference on Learning Representations},
    title={Fourier Neural Operator for Parametric Partial Differential Equations},
    year={2021},
}

Lixin Liu (lxliu@cse.cuhk.edu.hk) and Bangqi Fu (bqfu21@cse.cuhk.edu.hk)

Xplace is an open source project licensed under a BSD 3-Clause License that can be found in the LICENSE file.