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.
- 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)
Pytorch 0.4.1 or 1.0.0
Python 2.7 or Python 3.5
- Need to install and visible for linking
- Integrated as a git submodule
- Integrated as a submodule
CUDA 9.1 or later (Optional)
- If installed and found, GPU acceleration will be enabled.
- Otherwise, only CPU implementation is enabled.
- If installed and found, the plotting functions will be faster by using C/C++ implementation.
- Otherwise, python implementation is used.
- 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
CMake is adopted as the makefile system. To build, go to the root directory.
mkdir build cd build cmake .. 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
- CMAKE_CUDA_FLAGS: custom string for NVCC (default -gencode=arch=compute_60,code=sm_60)
How to Get Benchmarks
To get ISPD 2005 benchmarks, run the following script from the directory.
How to Run
Before running, make sure the benchmarks have been downloaded and the python dependency packages have been installed. Run with JSON configuration file for full placement in the root directory.
python dreamplace/Placer.py test/ispd2005/adaptec1.json
Test individual pytorch op with the unitest in the root directory.
Descriptions of options in JSON configuration file can be found by running the following command.
python dreamplace/Placer.py --help