OpenPARF

🕹 OpenPARF is an open-source FPGA placement and routing framework build upon the deep learning toolkit PyTorch. It is designed to be flexible, efficient, and extensible.

News

• 🎉 (2024/02/02) We are excited to announce the release of OpenPARF 2.0! This release includes a number of significant improvements and new features, including multi-die FPGA placement support and a new architecture definition format Flexshelf. We have also made a number of improvements to unit tests and documentation. We encourage all users to upgrade to this new version.

• OpenPARF
  • News
  • More About OpenPARF
    • A Multi-Electrostatic-based FPGA P&R Framework
    • Single-Die Reference Flow
    • Demo
  • Multi-Die Placement for OpenPARF
    • Brief Introduction
    • Multi-Die Reference Flow
  • Prerequisites
    • Build from Source
      • Install Dependencies
      • Install Gurobi (Optional)
    • Build with Docker
      • Docker Image
        • Using pre-built images
        • Building the image yourself
      • Running the Docker Image
      • Entering the Docker Container
  • Build and Install OpenPARF
    • Get the OpenPARF Source
    • Install OpenPARF
    • Adjust Build Options (Optional)
  • Getting Started
    • ISPD 2016/2017 Benchmarks
      • Obtaining Benchmarks
      • Linking Benchmarks
      • Running the Benchmarks
      • Adjust Benchmark Options (Optional)
      • More Advanced Usages
        • Running Benchmarks in Batches
        • Vivado Flow for Placement Evaluation
    • Customizing Multi-Die FPGA Architecture
      • Architecture Definition Format
      • Customizing the Architecture
      • Modify the SLL Counts Lookup Table (Optional)
        • Step 1: Generating a Custom SLL Counts Lookup Table
        • Step 2: Integrating the SLL Lookup Table into Your Project
  • Resources
  • Releases and Contributing
  • The Team
  • Publications
  • License
  • FPGA'24 Contest

More About OpenPARF

OpenPARF is an open-source framework for FPGA rapid placement and routing, which can run on both CPU and GPU. OpenPARF provides a number of APIs to enable researchers to quickly prototype their own FPGA algorithms and evaluate their performance on real FPGA hardware.

At a granular level, OpenPARF is a framework that consists of the following components:

OpenPARF provides a compilation stack to integrate your placement and routing algorithms into operators that can be used in Python. You can extend OpenPARF as needed, making it the ideal environment for FPGA optimization enthusiasts.

Elaborating Further:

A Multi-Electrostatic-based FPGA P&R Framework

OpenPARF is a powerful FPGA P&R framework that utilizes a multi-electrostatic-based approach to achieve optimal results with respect to routed wirelength and placement speed. OpenPARF supports the following features:

- A wide range of logical unit types including LUT, FF, DSP, BRAM, IO, Distributed RAM, and Shift Register
- Support for SLICEL and SLICEM CLB heterogeneous types
- Clock routing constraints
- Advanced support for multi-die FPGA placement
- ...

OpenPARF has proven to be a powerful tool, outperforming the state-of-the-art academic FPGA P&R tools in terms of wired length and placement speed. With a 0.4-12.7% improvement in routed wirelength and a speedup of over 2X in placement, OpenPARF is a highly efficient FPGA P&R framework that offers optimized results with minimal manual intervention.

Single-Die Reference Flow

Demo

The following are the visualization for electrostatic fields in benchmark ISPD2016/FPGA06.

LUT	FF	DSP	BRAM

Multi-Die Placement for OpenPARF

Brief Introduction

In the ongoing endeavor to enhance OpenPARF, we have integrated a key feature from the LEAPS - a specialized solution aimed at minimizing super long line (SLL) counts in multi-die FPGA placement. This integration not only enriches OpenPARF's capabilities but also addresses the complexities of modern multi-die FPGA designs.

Compared to the existing state-of-the-art method, OpenPARF demonstrates significant superiority, achieving an average reduction of 43.08% in the SLL counts and a 9.99% optimization in half-perimeter wirelength (HPWL). Notably, OpenPARF leverages GPU acceleration technology, achieving a remarkable runtime improvement of up to 34.34X.

Multi-Die Reference Flow

Prerequisites

OpenPARF is written in C++ and Python. The following are the prerequisites for building OpenPARF.

• Python 3.7 or above.
• C++ compiler with C++14 support, recommended to use GCC 7.5. Other version may also work, but have not been tested.
• PyTorch 1.7.1. Other version may also work, but have not been tested. Please refer to the next section to install PyTorch through conda environment.
• Gurobi 9.5 (optional, if the router is compiled). Other version may also work, but have not been tested. Please make sure to obtain a valid license and follow the installation instructions provided on the Gurobi website. Note that only the router uses gurobi in OpenPARF. If you do not need a router, you can leave gurobi uninstalled and set ENABL_ROUTER to OFF when compiling OpenPARF.
• NVIDIA CUDA 11.0 (optional, if compiled with CUDA support). Other versions may also work, but have not been tested. If CUDA is found, the project can run on the GPU implementation, otherwise it will only run on the CPU implementation.

We have provided two ways to build OpenPARF,

• one is to build from source,
• and the other is to build with docker.

Build from Source

Install Dependencies

We highly recommend installing an Anaconda or Mamba environment. You will get controlled dependency versions regardless of your Linux distro.

# * create and activate conda environment
mamba create --name openparf python=3.7
mamba activate openparf

# * common packages
mamba install cmake boost bison

# * Pytorch 1.7.1. Other version may also work, but have not been tested.
mamba install pytorch==1.7.1 torchvision==0.8.2 cudatoolkit=11.0 -c pytorch

# * python packages
pip install hummingbird-ml pyyaml networkx tqdm

Install Gurobi (Optional)

Permit me to illuminate the exquisite process of installing Gurobi, using the recommended Gurobi 9.5.1 as a quintessential example:

1. Download the gurobi9.5.1_linux64.tar.gz, and then extract it to a location of your choosing, aptly referred to as <your Gurobi home>.
2. Next, you must obtain a license for Gurobi. To do so, you must first create an account on the Gurobi website. Once you have created an account, you will be able to request an license. Once you receive your license, you will be able to download it from the Gurobi website. The license will be a file with the extension .lic. Save this file to a location of your choosing, aptly referred to as <your Gurobi license path>.
3. Finally, you must set the following environment variables. You can do so by adding the following lines to your ~/.bashrc file.

export GUROBI_HOME="<your Gurobi home>/linux64"
# For example,
# export GUROBI_HOME="/home/jingmai/softwares/gurobi951/linux64"
export GRB_LICENSE_FILE="<your Gurobi license path>"
# For example,
# export GRB_LICENSE_FILE="/home/jingmai/licenses/gurobi.lic"
export PATH="${PATH}:${GUROBI_HOME}/bin"
export LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:${GUROBI_HOME}/lib"

Now, please go to the Build and Install OpenPARF section to build and install OpenPARF.

Build with Docker

Docker Image

Using pre-built images

You can also pull a pre-built docker image from Docker Hub.

docker pull magic3007/openparf:2.0

Building the image yourself

You can also build the docker image yourself. The dockerfile is located in docker/openparf.dockerfile. To build the image, run the following command:

cd <source directory>/docker
docker build . -t openparf:2.0 -f openparf.dockerfile

Running the Docker Image

We recommend that you have the following two directories/files on your host before running docker:

1. <source directory on host>: The directory where you store the OpenPARF source code on host. See Get the OpenPARF Source for more details.
2. <benchmark directory on host>: The directory where you store the ISPD 2016/2017 benchmarks on host. See Obtaining Benchmarks for more details.

Without CUDA Support To run the docker image without CUDA support, run the following command:

docker run -itd --restart=always --network host -e TERM=$TERM \
  --name openparf \
  -v /etc/localtime:/etc/localtime:ro \
  -v <project directory on host>:/root/OpenPARF \
  -v <benchmark directory on host>:/root/benchmarks \
  openparf:2.0 \
  /bin/bash;

With CUDA Support To run the docker image with CUDA support, run the following command:

docker run -itd --restart=always --network host -e TERM=$TERM \
  --name openparf \
  --gpus all \
  -v /etc/localtime:/etc/localtime:ro \
  -v <project directory on host>:/root/OpenPARF \
  -v <benchmark directory on host>:/root/benchmarks \
  openparf:2.0 \
  /bin/bash;

Entering the Docker Container

Once the docker image is running, you can enter the docker container by running the following command:

docker exec -it openparf /bin/bash

Within the docker container:

• the OpenPARF source code will be located in /root/OpenPARF.
• the ISPD 2016/2017 benchmarks will be located in /root/benchmarks.

NOTE that though we have gurobi 9.5.1 installed in docker (located in /opt/gurobi), due to permission reasons, if you need router, you still need to get gurobi license and place it as /root/gurobi.lic.

Now, please go to the Build and Install OpenPARF section to build and install OpenPARF.

Build and Install OpenPARF

Get the OpenPARF Source

git clone --recursive https://github.com/PKU-IDEA/OpenPARF.git

If you have already clone the repository, e.g., the repository is mounted in the docker container, you can skip this step.

Install OpenPARF

Assuming that OpenAPRF is a subfolder in the current directory, i.e., ./OpenPARF is the path to the OpenPARF source code.

mkdir build
cd build
cmake ../OpenPARF -DCMAKE_PREFIX_PATH=$CONDA_PREFIX -DPYTHON_EXECUTABLE=$(which python) -DPython3_EXECUTABLE=$(which python) -DCMAKE_INSTALL_PREFIX=<installation directory>
make -j8
make install

Where <installation directory> is the directory where you want to install OpenPARF (e.g., ../install).

Adjust Build Options (Optional)

You can adjust the configuration of cmake variables optionally (without buiding first), by doing the following.

• CMAKE_INSTALL_PREFIX: The directory where you want to install OpenPARF (e.g., ../install).
• CMAKE_BUILD_TYPE: The build type, can be Release or Debug. Default is Release.
• USE_CCACHE: Whether to use ccache to speed up compilation. Default is OFF.
• ENABLE_ROUTER: Whether to compile the router. Default is OFF. If you need a router, you can set it to ON.

Getting Started

ISPD 2016/2017 Benchmarks

Obtaining Benchmarks

To obtain the benchmarks, you can download the benchmark zip files from the provided Google Drive links. There are separate links for ISPD 2016 and ISPD 2017 FPGA Placement Benchmarks, along with their respective Flexshelf versions.

Note: The Flexshelf versions of these benchmarks draw upon the architectural description files from the VTR project as a foundational reference. This approach not only provides a comprehensive and realistic benchmarking environment but also facilitates easy customization of architectures, making it particularly beneficial for advanced FPGA designs that require tailored configurations.

• ISPD 2016 FPGA Placement Benchmarks [Google Drive / Baidu Drive]

• ISPD 2016 FPGA Placement Flexshelf Benchmarks [Google Drive / Baidu Drive]

• ISPD 2017 FPGA Placement Benchmarks [Google Drive / Baidu Drive]

• ISPD 2017 FPGA Placement Flexshelf Benchmarks [Google Drive / Baidu Drive]

💡 Toggle to see how to download files from Google Drive in command line

1. Click on the download button in the browser (e.g. Chrome)

2. Copy the download link from the browser

3. Use the curl command in the terminal to download the file

curl <url> --output <output filename>

Once the files have downloaded, extract their contents to the <benchmark directory> folder. Under this directory, you should then have two new directories, one for ISPD 2016 and another for ISPD 2017. Remember to keep these benchmark directories separate as OpenPARF supports ISPD2016 and ISPD2017 benchmarks.

<benchmark directory>
├── ispd2016
├── ispd2016_flexshelf
├── ispd2017
├── ispd2017_flexshelf

Linking Benchmarks

Link ispd2016 and ispd2017 folders under <installation directory>/benchmarks by soft link.

ln -s <benchmark directory>/ispd2016 <installation directory>/benchmarks/ispd2016
ln -s <benchmark directory>/ispd2016_flexshelf <installation directory>/benchmarks/ispd2016_flexshelf
ln -s <benchmark directory>/ispd2017 <installation directory>/benchmarks/ispd2017
ln -s <benchmark directory>/ispd2017_flexshelf <installation directory>/benchmarks/ispd2017_flexshelf

Running the Benchmarks

To run the benchmarks, navigate to the installation directory and execute the following command:

cd <installation directory>
python openparf.py --config unittest/regression/ispd2016_flexshelf/FPGA01_flexshelf.json

Note that the openparf.py script requires the --config option to specify the configuration file to use. The appropriate configuration file for the benchmark should exist in the corresponding directory (unittest/regression/ispd2016_flexshelf/) before running the command.

• For Single-Die Place & Route, use scripts from unittest/regression/ispd2016, unittest/regression/ispd2016_flexshelf, unittest/regression/ispd2017, and unittest/regression/ispd2017_flexshelf.
• For Multi-Die Place, use scripts from unittest/regression/ispd2016_flexshelf and unittest/regression/ispd2017_flexshelf.

It is essential to ensure all dependencies and the Python environment have been correctly set up before running the command. Once everything is in order, the benchmark will commence, and the outcomes will be sent to the output directory.

Adjust Benchmark Options (Optional)

OpenPARF allows configuration of benchmark parameters using JSON. Default parameters can be found in openparf/params.json, while users can use --config to pass custom parameters which will override defaults. For example:

python openparf.py --config unittest/regression/ispd2016_flexshelf/FPGA01_flexshelf.json

The parameter configuration in unittest/regression/ispd2016_flexshelf/FPGA01_flexshelf.json will override the defaults in openparf/params.json.

Common modifiable parameters include

• gpu: Enable GPU acceleration or not. Default is 0.
• result_dir: Specify the directory to save P&R results.
• route_flag: Enable router or not. Default is 0.
• slr_aware_flag: Enable multi-die placer or not. Default is 1.

For more detailed information on parameters, please see the description field in openparf/params.json.

More Advanced Usages

More advanced usages are available to customize the benchmark run.

Running Benchmarks in Batches

We also provide scripts to run ISPD2016 and ISPD2017 in batches. Navigate to the installation directory and execute the following commands:

cd <installation directory>
<source directory>/scripts/run_ispd2016_benchmark.sh # ispd 2016
<source directory>/scripts/run_ispd2016_flexshelf_benchmark.sh # ispd 2016 flexshelf
<source directory>/scripts/run_ispd2017_benchmark.sh # ispd 2017
<source directory>/scripts/run_ispd2017_flexshelf_benchmark.sh # ispd 2017 flexshelf

The results can be found in <installation directory>/../ispd2016_log, <installation directory>/../ispd2016_flexshelf_log, <installation directory>/../ispd2017_log and <installation directory>/../ispd2017_flexshelf_log, respectively. Please refer to the script for specific configurations.

Vivado Flow for Placement Evaluation

If you are looking to evaluate a placement algorithm, you can do so directly with OpenPARF. Alternatively, you can import the placement results from OpenPARF into Vivado for evaluation. This is particularly useful if you are competing in the ISPD 2016/2017 contest and want to use the Vivado flow for placement evaluation. The official websites for ISPD offer instructions on how to load the placement results into the Vivado flow.

When you import the OpenPARF placement result file into Vivado, it will be located in <result dir>/<benchmark name>.pl (e.g., results/FPGA01/FPGA01.pl). Keep in mind that the <result dir> and <benchmark name> are parameters set within the JSON configuration.

NOTE that if you want the evaluate the placement via Vivado, you can disable the routing stage in OpenPARF by simply setting the route_flag to 0 before running the tool.

Customizing Multi-Die FPGA Architecture

In OpenPARF, customizing the architecture of multi-die FPGA is a vital feature that allows for greater flexibility and adaptability in FPGA design. Below, we provide detailed instructions on how to define and modify the architecture to suit specific design requirements.

Architecture Definition Format

OpenPARF uses a structured XML format for defining the architecture of multi-die FPGAs. These architecture files can be found at benchmarks/arch/ultrascale/multi-die_layout_<{num_cols}x{num_rows}>.xml. The format includes several key tags such as <resources>, <primitives>, <global_sw>, <tile_blocks>, <cores>, and <chip>. The <chip> tag is crucial as it describes the topology of SLRs and their types.

Example of a 2x2 SLR topology in XML format:

<chip name="demo_chip">
    <grid name="chip_grid" cols="2" rows="2">
        <core name="CORE_0" type="CORE_A" x="0" y="0" width="168" height="120"/>
        <core name="CORE_1" type="CORE_A" x="0" y="1" width="168" height="120"/>
        <core name="CORE_2" type="CORE_B" x="1" y="0" width="168" height="120"/>
        <core name="CORE_3" type="CORE_B" x="1" y="1" width="168" height="120"/>
    </grid>
</chip>

Customizing the Architecture

To customize the multi-die FPGA architecture in OpenPARF:

• Define the SLR Topology: Adjust the <grid> tag within the <chip> element to match your desired SLR topology, specifying the cols and rows.

• Specify Core Details: Each core within the grid should have its name, type, x, y, width, and height attributes defined, aligning with the SLR specifications in the <cores> section.

• Adjust Resources and Constraints: Customize settings in the <resources>, <primitives>, <global_sw>, and <tile_blocks> tags to suit your topology and core details. Implement specific customizations or adhere to default settings if they meet your needs.

• Recheck the Architecture: Always recheck the customized architecture for correctness and alignment with your design requirements.

Following these guidelines will enable users to customize the architecture of multi-die FPGAs in OpenPARF, enhancing the adaptability and efficiency of FPGA designs.

Modify the SLL Counts Lookup Table (Optional)

When customizing the architecture of a multi-die FPGA, especially for SLR topologies beyond the predefined 1x4 or 2x2 layouts, it's crucial to also update the SLL counts lookup table to align with the new architecture. This step ensures that the FPGA design accurately reflects the customized topology.

Step 1: Generating a Custom SLL Counts Lookup Table

Run the scripts/compute_sll_counts_table.py script with your specific SLR topology dimensions (num_cols and num_rows).

Example command:

python compute_sll_counts_table.py --num_cols <num cols> --num_rows <num rows> --output <filename>

• Replace <filename> with your chosen file name, <num cols> and <num rows> with the number of columns and rows in your SLR topology.
• After running the script, a file named <filename>.npy will be generated. Move this file to the directory <installation directory>/openparf/ops/sll/.

Note: Typically, num_cols and num_rows should not exceed 5 due to fabrication and technology constraints. The script is optimized for efficient execution within this scale.

Step 2: Integrating the SLL Lookup Table into Your Project

To use the newly generated SLL counts lookup table in your project, modify the code in the file located at <install>/openparf/ops/sll/sll.py as follows:

• Locate the section of the code initializing the SLL counts table. It will typically have predefined tables for 1x4 and 2x2 SLR topologies.
• For SLR topologies other than 1x4 and 2x2, you will see a placeholder where the table should be loaded. Uncomment and modify this section:

  else:
    self.sll_counts_table = torch.from_numpy(
        np.load(
            os.path.join(
                os.path.dirname(os.path.abspath(__file__)),
                "<filename>.npy"))).to(dtype=torch.int32)
  

  Replace <filename> with the name of your generated .npy file.

This process will replace the default lookup table with the one you generated, tailoring the functionality to your specific SLR topology.

Resources

• ISPD 2016: Routability-Driven FPGA Placement Contest
  • Official ISPD 2016 FPGA Placement benchmarks can be downloaded from here.
• ISPD 2017: Clock-Aware FPGA Placement Contest

Releases and Contributing

We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion.

If you plan to contribute new features, utility functions or extensions, please first open an issue and discuss the feature with us. Sending a PR without discussion might end up resulting in a rejected PR, because we might be taking the core library in a different direction than you might be aware of.

The Team

OpenPARF is maintained by PKU-IDEA Lab at Peking University, supervised by Prof. Yibo Lin.

• Jing Mai, Jiarui Wang and Yibai Meng composed the initial release.

• Runzhe Tao developed and integrated the LEAPS for multi-die plcaement in OpenPARF.

Publications

• Jing Mai, Jiarui Wang, Zhixiong Di, Guojie Luo, Yun Liang and Yibo Lin, "OpenPARF: An Open-Source Placement and Routing Framework for Large-Scale Heterogeneous FPGAs with Deep Learning Toolkit", International Conference on ASIC (ASICON), 2023. [paper]
• Jing Mai, Yibai Meng, Zhixiong Di, and Yibo Lin, “Multi-electrostatic FPGA placement considering SLICEL-SLICEM heterogeneity and clock feasibility,” in Proceedings of the 59th ACM/IEEE Design Automation Conference (DAC), San Francisco California: ACM, Jul. 2022, pp. 649–654. doi: 10.1145/3489517.3530568. [paper]
• Jiarui Wang, Jing Mai, Zhixiong Di, and Yibo Lin, “A Robust FPGA Router with Concurrent Intra-CLB Rerouting,” in Proceedings of the 28th Asia and South Pacific Design Automation Conference (ASP-DAC), Tokyo Japan: ACM, Jan. 2023, pp. 529–534. doi: 10.1145/3566097.3567898. [paper]
• Zhixiong Di, Runzhe Tao, Jing Mai, Lin Chen, Yibo Lin, "LEAPS:Topological-Layout-Adaptable Multi-die FPGA Placement for Super Long Line Minimization", IEEE Transactions on Circuits and Systems I: Regular Papers, doi: 10.1109/TCSI.2023.3340554, 2023. [paper]

@inproceedings{PLACE_ASICON23_Mai_OpenPARF,
  title         = {OpenPARF: An Open-Source Placement and Routing Framework for Large-Scale Heterogeneous FPGAs with Deep Learning Toolkit},
  author        = {Jing Mai and Jiarui Wang and Zhixiong Di and Guojie Luo and Yun Liang and Yibo Lin},
  booktitle     = {International Conference on ASIC (ASICON)}
  year          = {2023},
}

@inproceedings{PLACE_DAC22_Mai,
  title         = {Multi-electrostatic {FPGA} placement considering {SLICEL-SLICEM} heterogeneity and clock feasibility},
  author        = {Jing Mai and Yibai Meng and Zhixiong Di and Yibo Lin},
  booktitle     = {ACM/IEEE Design Automation Conference (DAC)},
  pages         = {649--654},
  year          = {2022},
}

@inproceedings{ROUTE_ASPDAC2023_Wang,
  title         = {{{A Robust FPGA Router with Concurrent Intra-CLB Rerouting}}},
  author        = {Jiarui Wang and Jing Mai and Zhixiong Di and Yibo Lin},
  booktitle     = {IEEE/ACM Asia and South Pacific Design Automation Conference (ASPDAC)},
  pages         = {529--534},
  year          = {2023}
}

@article{PLACE_TCASI2023_Di,
  title         ={LEAPS: Topological-Layout-Adaptable Multi-Die FPGA Placement for Super Long Line Minimization},
  author        ={Di, Zhixiong and Tao, Runzhe and Mai, Jing and Chen, Lin and Lin, Yibo},
  journal       ={IEEE Transactions on Circuits and Systems I: Regular Papers},
  year          ={2023},
  publisher     ={IEEE}
}

License

This software is released under BSD 3-Clause License, Please refer to LICENSE for details.

FPGA'24 Contest

View fpga24contest folder for our routing solution for FPGA'24 Contest.