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.
Bigblue4 | Density Map | Electric Potential | Electric Field |
---|---|---|---|
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
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)
Yibo Lin, David Z. Pan, Haoxing Ren and Brucek Khailany, "DREAMPlace 2.0: Open-Source GPU-Accelerated Global and Detailed Placement for Large-Scale VLSI Designs", China Semiconductor Technology International Conference (CSTIC), Shanghai, China, Jun, 2020 (preprint)(Invited Paper)
Jiaqi Gu, Zixuan Jiang, Yibo Lin and David Z. Pan, "DREAMPlace 3.0: Multi-Electrostatics Based Robust VLSI Placement with Region Constraints", IEEE/ACM International Conference on Computer-Aided Design (ICCAD), Nov 2-5, 2020 (preprint)
Peiyu Liao, Siting Liu, Zhitang Chen, Wenlong Lv, Yibo Lin and Bei Yu, "DREAMPlace 4.0: Timing-driven Global Placement with Momentum-based Net Weighting", IEEE/ACM Proceedings Design, Automation and Test in Eurpoe (DATE), Antwerp, Belgium, Mar 14-23, 2022 (preprint)
Yifan Chen, Zaiwen Wen, Yun Liang, Yibo Lin, "Stronger Mixed-Size Placement Backbone Considering Second-Order Information", IEEE/ACM International Conference on Computer-Aided Design (ICCAD), San Francisco, CA, Oct, 2023 (preprint)
Python 3.5/3.6/3.7/3.8/3.9
Pytorch 1.6/1.7/1.8/2.0
c++17
support).Boost >= 1.55.0
Bison >= 3.3
CUDA 9.1 or later (Optional)
GPU architecture compatibility 6.0 or later (Optional)
Cairo (Optional)
NTUPlace3 (Optional)
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
Go to the root directory.
pip install -r requirements.txt
Two options are provided for building: with and without Docker.
You can use the Docker container to avoid building all the dependencies yourself.
docker pull limbo018/dreamplace:cuda
docker build . --file Dockerfile --tag your_name/dreamplace:cuda
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 $(pwd):/DREAMPlace limbo018/dreamplace:cuda bash
Run without GPU on Windows.
docker run -it -v /dreamplace limbo018/dreamplace:cuda bash
cd /DREAMPlace
.CMake is adopted as the makefile system. To build, go to the root directory.
mkdir build
cd build # we call this <build directory>
cmake .. -DCMAKE_INSTALL_PREFIX=<installation directory> -DPython_EXECUTABLE=$(which python)
make
make install
Where <build directory>
is the directory where you compile the code, and <installation directory>
is the directory where you want to install DREAMPlace (e.g., ../install
).
Third party submodules are automatically built except for Boost.
To clean, go to the root directory.
rm -r build
<build directory>
can be removed after installation if you do not need incremental compilation later.
Here are the available options for CMake.
cmake -DCMAKE_INSTALL_PREFIX=path/to/your/directory
cmake -DCMAKE_CUDA_FLAGS=-gencode=arch=compute_60,code=sm_60
cmake -DCMAKE_CXX_ABI=0
To get ISPD 2005 and 2015 benchmarks, run the following script from the directory.
python benchmarks/ispd2005_2015.py
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.
cd <installation directory>
python dreamplace/Placer.py test/ispd2005/adaptec1.json
Test individual pytorch
op with the unit tests in the root directory.
cd <installation directory>
python unittest/ops/hpwl_unittest.py
Descriptions of options in JSON configuration file can be found by running the following command.
cd <installation directory>
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 |
...
Recently, many studies chose DREAMPLace for macro placement, e.g., [Cheng+, NeurIPS2021], [Lai+, NeurIPS2023], etc. However, the results reported on the same benchmarks vary significantly from one work to another. For better comparison, we provide the results collected from our GPU machine for reference. If your results deviate significantly (i.e., >5% longer HPWL) from the following numbers, something may be wrong. We recommend you to contact us with logs for validation.
Note that DREAMPlace 4.1.0 only implements the BB step and 2-stage flow proposed in [Chen+, ICCAD2023].
ISPD2005 benchmark with all fixed macros and IO pads regarded as movable macros
DREAMPlace 4.0 | DREAMPlace 4.1.0 | |||||
---|---|---|---|---|---|---|
Iterations | HPWL(x10^6) | Time(s) | Iterations | HPWL(x10^6) | Time(s) | |
adaptec1 | 600 | 101.3 | 26.3 | 748 | 68.2 | 27.6 |
adaptec2 | 588* | 137.5* | 40.6* | 784 | 86.3 | 40.1 |
adaptec3 | 765 | 179.5 | 54.1 | 894 | 144.0 | 56.1 |
adaptec4 | 876 | 153.3 | 48.9 | 872 | 140.8 | 57.3 |
bigblue1 | 699 | 86.2 | 23.5 | 813 | 82.0 | 25.5 |
bigblue2 | 1267* | 2426.7* | 679.4* | 869 | 98.1 | 193.4 |
bigblue3 | 1207 | 330.2 | 115.4 | 1307 | 288.8 | 140.1 |
bigblue4 | 1581 | 820.1 | 239.6 | 1875 | 610.0 | 234.5 |
average ratio | 0.937 | 4.211 | 1.258 | 1.000 | 1.000 | 1.000 |
MMS benchmark (modified from ISPD2005 benchmarks with movable macros and fixed IO pads)
Our modified version can be downloaded from here.
DREAMPlace 4.0 | DREAMPlace 4.1.0 | |||||
---|---|---|---|---|---|---|
Iterations | HPWL(x10^6) | Time(s) | Iterations | HPWL(x10^6) | Time(s) | |
adaptec1 | 607 | 65.3 | 17.8 | 746 | 64.7 | 25.8 |
adaptec2 | 569 | 79.3 | 28.5 | 734 | 75.8 | 35.8 |
adaptec3 | 659 | 158.1 | 44.6 | 755 | 153.3 | 38.9 |
adaptec4 | 735 | 141.7 | 46.8 | 782 | 142.4 | 47.5 |
adaptec5 | 1053 | 326.3 | 63.8 | 1405 | 337.6 | 78.4 |
bigblue1 | 646 | 85.4 | 21.3 | 809 | 85.3 | 28.9 |
bigblue2 | 638 | 125.3 | 42.0 | 773 | 125.4 | 48.4 |
bigblue3 | 911 | 279.3 | 112.5 | 1097 | 273.8 | 136.1 |
bigblue4 | 1189 | 648.8 | 172.4 | 1515 | 643.2 | 215.4 |
newblue1 | 574 | 62.8 | 22.5 | 749 | 62.0 | 30.4 |
newblue2 | 730 | 155.5 | 34.8 | 861 | 156.1 | 43.9 |
newblue3 | 1318* | 597.3* | 55.71* | 830 | 270.6 | 72.8 |
newblue4 | 1009 | 246.2 | 52.6 | 1274 | 245.8 | 53.9 |
newblue5 | 1254 | 444.2 | 99.4 | 1537 | 446.4 | 134.9 |
newblue6 | 929 | 410.6 | 96.1 | 1157 | 409.3 | 115.1 |
newblue7 | 1077 | 903.6 | 184.1 | 1578 | 903.2 | 235.1 |
average ratio | 0.855 | 1.081 | 0.830 | 1.000 | 1.000 | 1.000 |
*
denotes divergence or legalization failure.
Note that if you observe divergence or legalization errors in the log, then the results may not be representative.