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DREAMPlace

Deep learning toolkit-enabled VLSI placement. With the analogy between nonlinear VLSI placement and deep learning training problem, this tool is developed with deep learning toolkit for flexibility and efficiency. The tool runs on both CPU and GPU. Over 30X speedup over the CPU implementation (RePlAce) is achieved in global placement and legalization on ISPD 2005 contest benchmarks with a Nvidia Tesla V100 GPU. DREAMPlace also integrates a GPU-accelerated detailed placer, ABCDPlace, which can achieve around 16X speedup on million-size benchmarks over the widely-adopted sequential placer NTUPlace3 on CPU.

DREAMPlace runs on both CPU and GPU. If it is installed on a machine without GPU, only CPU support will be enabled with multi-threading.

  • Animation
Bigblue4 Density Map Electric Potential Electric Field
Density Map Electric Potential Map Electric Field Map
  • Reference Flow

Publications

  • Yibo Lin, Shounak Dhar, Wuxi Li, Haoxing Ren, Brucek Khailany and David Z. Pan, "DREAMPlace: Deep Learning Toolkit-Enabled GPU Acceleration for Modern VLSI Placement", ACM/IEEE Design Automation Conference (DAC), Las Vegas, NV, Jun 2-6, 2019 (preprint) (slides)

  • Yibo Lin, Zixuan Jiang, Jiaqi Gu, Wuxi Li, Shounak Dhar, Haoxing Ren, Brucek Khailany and David Z. Pan, "DREAMPlace: Deep Learning Toolkit-Enabled GPU Acceleration for Modern VLSI Placement", IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2020 (accepted)

  • Yibo Lin, Wuxi Li, Jiaqi Gu, Haoxing Ren, Brucek Khailany and David Z. Pan, "ABCDPlace: Accelerated Batch-based Concurrent Detailed Placement on Multi-threaded CPUs and GPUs", IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2020 (preprint) (accepted)

Dependency

  • Python 3.5/3.6/3.7

  • Pytorch 1.0.0

    • Other version around 1.0.0 may also work, but not tested
  • GCC

    • Recommend GCC 5.1 or later.
    • Other compilers may also work, but not tested.
  • Boost

    • Need to install and visible for linking
  • Limbo

    • Integrated as a git submodule
  • Flute

    • Integrated as a submodule
  • CUB

    • Integrated as a git submodule
  • munkres-cpp

    • Integrated as a git submodule
  • CUDA 9.1 or later (Optional)

    • If installed and found, GPU acceleration will be enabled.
    • Otherwise, only CPU implementation is enabled.
  • GPU architecture compatibility 6.0 or later (Optional)

    • Code has been tested on GPUs with compute compatibility 6.0, 7.0, and 7.5.
    • Please check the compatibility of the GPU devices.
    • The default compilation target is compatibility 6.0. This is the minimum requirement and lower compatibility is not supported for the GPU feature.
    • For compatibility 7.0, it is necessary to set the CMAKE_CUDA_FLAGS to -gencode=arch=compute_70,code=sm_70.
  • Cairo (Optional)

    • If installed and found, the plotting functions will be faster by using C/C++ implementation.
    • Otherwise, python implementation is used.
  • NTUPlace3 (Optional)

    • If the binary is provided, it can be used to perform detailed placement.

To pull git submodules in the root directory

git submodule init
git submodule update

Or alternatively, pull all the submodules when cloning the repository.

git clone --recursive https://github.com/limbo018/DREAMPlace.git

How to Install Python Dependency

Go to the root directory.

pip install -r requirements.txt 

How to Build

Two options are provided for building: with and without Docker.

Build with Docker

You can use the Docker container to avoid building all the dependencies yourself.

  1. Install Docker on Windows, Mac or Linux.
  2. To enable the GPU features, install NVIDIA-docker; otherwise, skip this step.
  3. Navigate to the repository.
  4. Get the docker container with either of the following options.
    docker pull limbo018/dreamplace:cuda
    
    • Option 2: build the container.
    docker build . --file Dockerfile --tag your_name/dreamplace:cuda
    
  5. Enter bash environment of the container. Replace limbo018 with your name if option 2 is chosen in the previous step.

Run with GPU on Linux.

docker run --gpus 1 -it -v $(pwd):/DREAMPlace limbo018/dreamplace:cuda bash

Run with GPU on Windows.

docker run --gpus 1 -it -v /dreamplace limbo018/dreamplace:cuda bash

Run without GPU on Linux.

docker run -it -v $(pwd):/DREAMPlace limbo018/dreamplace:cuda bash

Run without GPU on Windows.

docker run -it -v /dreamplace limbo018/dreamplace:cuda bash
  1. cd /DREAMPlace.
  2. Go to next section to complete building.

Build without Docker

CMake is adopted as the makefile system. To build, go to the root directory.

mkdir build 
cd build 
cmake .. -DCMAKE_INSTALL_PREFIX=your_install_path
make 
make install

Third party submodules are automatically built except for Boost.

To clean, go to the root directory.

rm -r build

Here are the available options for CMake.

  • CMAKE_INSTALL_PREFIX: installation directory
    • Example cmake -DCMAKE_INSTALL_PREFIX=path/to/your/directory
  • CMAKE_CUDA_FLAGS: custom string for NVCC (default -gencode=arch=compute_60,code=sm_60)
    • Example cmake -DCMAKE_CUDA_FLAGS=-gencode=arch=compute_60,code=sm_60
  • CMAKE_CXX_ABI: 0|1 for the value of _GLIBCXX_USE_CXX11_ABI for C++ compiler, default is 0.
    • Example cmake -DCMAKE_CXX_ABI=0
    • It must be consistent with the _GLIBCXX_USE_CXX11_ABI for compling all the C++ dependencies, such as Boost and PyTorch.
    • PyTorch in default is compiled with _GLIBCXX_USE_CXX11_ABI=0, but in a customized PyTorch environment, it might be compiled with _GLIBCXX_USE_CXX11_ABI=1.

How to Get Benchmarks

To get ISPD 2005 and 2015 benchmarks, run the following script from the directory.

python benchmarks/ispd2005_2015.py

How to Run

Before running, make sure the benchmarks have been downloaded and the python dependency packages have been installed. Go to the install directory and run with JSON configuration file for full placement.

python dreamplace/Placer.py test/ispd2005/adaptec1.json

Test individual pytorch op with the unitest in the root directory.

python unitest/ops/hpwl_unitest.py

Configurations

Descriptions of options in JSON configuration file can be found by running the following command.

python dreamplace/Placer.py --help

The list of options as follows will be shown.

JSON Parameter Default Description
aux_input required for Bookshelf input .aux file
lef_input required for LEF/DEF input LEF file
def_input required for LEF/DEF input DEF file
verilog_input optional for LEF/DEF input VERILOG file, provide circuit netlist information if it is not included in DEF file
gpu 1 enable gpu or not
num_bins_x 512 number of bins in horizontal direction
num_bins_y 512 number of bins in vertical direction
global_place_stages required global placement configurations of each stage, a dictionary of {"num_bins_x", "num_bins_y", "iteration", "learning_rate"}, learning_rate is relative to bin size
target_density 0.8 target density
density_weight 1.0 initial weight of density cost
gamma 0.5 initial coefficient for log-sum-exp and weighted-average wirelength
random_seed 1000 random seed
result_dir results result directory for output
scale_factor 0.0 scale factor to avoid numerical overflow; 0.0 means not set
ignore_net_degree 100 ignore net degree larger than some value
gp_noise_ratio 0.025 noise to initial positions for global placement
enable_fillers 1 enable filler cells
global_place_flag 1 whether use global placement
legalize_flag 1 whether use internal legalization
detailed_place_flag 1 whether use internal detailed placement
stop_overflow 0.1 stopping criteria, consider stop when the overflow reaches to a ratio
dtype float32 data type, float32
detailed_place_engine external detailed placement engine to be called after placement
detailed_place_command -nolegal -nodetail commands for external detailed placement engine
plot_flag 0 whether plot solution or not
RePlAce_ref_hpwl 350000 reference HPWL used in RePlAce for updating density weight
RePlAce_LOWER_PCOF 0.95 lower bound ratio used in RePlAce for updating density weight
RePlAce_UPPER_PCOF 1.05 upper bound ratio used in RePlAce for updating density weight
random_center_init_flag 1 whether perform random initialization around the center for global placement
sort_nets_by_degree 0 whether sort nets by degree or not
num_threads 8 number of CPU threads
dump_global_place_solution_flag 0 whether dump intermediate global placement solution as a compressed pickle object
dump_legalize_solution_flag 0 whether dump intermediate legalization solution as a compressed pickle object

Authors

  • Yibo Lin, supervised by David Z. Pan, composed the initial release.
  • Zixuan Jiang and Jiaqi Gu improved the efficiency of the wirelength and density operators on GPU.
  • Yibo Lin and Jiaqi Gu developed and integrated ABCDPlace for detailed placement.
  • Pull requests to improve the tool are more than welcome. We appreciate all kinds of contributions from the community.

Features

  • 0.0.2

    • Multi-threaded CPU and optional GPU acceleration support
  • 0.0.5

    • Net weighting support through .wts files in Bookshelf format
    • Incremental placement support
  • 0.0.6

    • LEF/DEF support as input/output
    • Python binding and access to C++ placement database
  • 1.0.0

    • Improved efficiency for wirelength and density operators from TCAD extension
  • 1.1.0

    • Docker container for building environment
  • 2.0.0

    • Integrate ABCDPlace: multi-threaded CPU and GPU acceleration for detailed placement
    • Support independent set matching, local reordering, and global swap with run-to-run determinism on one machine
    • Support movable macros with Tetris-like macro legalization and min-cost flow refinement
  • 2.1.0

    • Support deterministic mode to ensure run-to-run determinism with minor runtime overhead
  • 2.2.0

    • Integrate routability optimization relying on NCTUgr from TCAD extension
    • Improved robustness on parallel CPU version