AutoDMP: Automated DREAMPlace-based Macro Placement

Built upon the GPU-accelerated global placer DREAMPlace and detailed placer ABCDPlace, AutoDMP adds simultaneous macro and standard cell placement enhancements and automatic parameter tuning based on multi-objective hyperparameter Bayesian optimization (MOBO).

Simultaneous Macro and Standard Cell Placement Animations

MemPool Group	Ariane

Publications

Anthony Agnesina, Puranjay Rajvanshi, Tian Yang, Geraldo Pradipta, Austin Jiao, Ben Keller, Brucek Khailany, and Haoxing Ren, "AutoDMP: Automated DREAMPlace-based Macro Placement", International Symposium on Physical Design (ISPD), Virtual Event, Mar 26-29, 2023 (preprint) (blog)

Dependency

DREAMPlace
- Commit b8f87eec1f4ddab3ad50bbd43cc5f4ccb0072892
- Other versions may also work, but not tested
GPU architecture compatibility 6.0 or later (Optional)
- Code has been tested on GPUs with compute compatibility 8.0 on DGX A100 machine.

How to Build

You can build in two ways:

Build without Docker by following the instructions of the DREAMPlace build at README_DREAMPlace.md.
Use the provided Dockerfile to build an image with the required library dependencies.

How to Run Multi-Objective Bayesian Optimization

To run the test of multi-objective Bayesian optimization on NVDLA NanGate45, call:

./tuner/run_tuner.sh 1 1 test/nvdla_nangate45_51/configspace.json test/nvdla_nangate45_51/NV_NVDLA_partition_c.aux test/nvdla_nangate45_51/nvdla_ppa.json \"\" 20 2 0 0 10 ./tuner test/nvdla_nangate45_51/mobohb_log

This will run on the GPUs for 20 iterations with 2 parallel workers. The different settings for the Bayesian optimization can be found in tuner/run_tuner.sh. The easiest way to explore different search spaces is to modify tuner/configspace.json. You can also run in single-objective mode or modify the parameters of the kernel density estimators in tuner/tuner_train.py.

Physical Design Flow