Skip to content

Implementation of NeurIPS 2021 paper "On Joint Learning for Solving Placement and Routing in Chip Design".

License

Notifications You must be signed in to change notification settings

Rogerity/EDA-AI

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

4 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

DeepPlace

An end-to-end learning approach DeepPlace for placement problem with two stages. The deep reinforcement learning (DRL) agent places the macros sequentially, followed by a gradient-based optimization placer to arrange millions of standard cells. We use PPO for all the experiments implemented with Pytorch, and the GPU version of DREAMPlace is adopted as gradient based optimization placer for arranging standard cells.

Requirements

In order to install requirements, follow:

# PyTorch
conda install pytorch torchvision -c soumith

# Baselines for Atari preprocessing
git clone https://github.com/openai/baselines.git
cd baselines
pip install -e .

# Other requirements
pip install -r requirements.txt

# DREAMplace installation
git clone --recursive https://github.com/limbo018/DREAMPlace.git
mkdir build 
cd build 
cmake .. -DCMAKE_INSTALL_PREFIX=your_install_path -DPYTHON_EXECUTABLE=$(which python)
make 
make install

#Get benchmarks
python benchmarks/ispd2005_2015.py

# DGL installation
conda install -c dglteam dgl-cuda10.2

Training

Macro Placement

python main.py --task "place" --algo ppo --use-gae --lr 2.5e-4 --clip-param 0.1 --value-loss-coef 0.5 --num-processes 1 --num-steps 2840 --num-mini-batch 4 --log-interval 1 --use-linear-lr-decay --entropy-coef 0.01

Joint Macro/Standard cell Placement

python EDA-AI/main.py --task "fullplace" --algo ppo --use-gae --lr 2.5e-4 --clip-param 0.1 --value-loss-coef 0.5 --num-processes 1 --num-steps 2840 --num-mini-batch 4 --log-interval 1 --use-linear-lr-decay --entropy-coef 0.01

Validation

python validation.py --task "place" --num-processes 1 --num-mini-batch 1 --num-steps 710 --lr 2.5e-4 --clip-param 0.1 --value-loss-coef 0.5 --entropy-coef 0.01

Results

pretraining

fullplace

About

Implementation of NeurIPS 2021 paper "On Joint Learning for Solving Placement and Routing in Chip Design".

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Prolog 74.9%
  • C++ 13.7%
  • Cuda 5.8%
  • Python 5.1%
  • Other 0.5%