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 |
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- 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)
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- Commit b8f87eec1f4ddab3ad50bbd43cc5f4ccb0072892
- Other versions may also work, but not tested
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GPU architecture compatibility 6.0 or later (Optional)
- Code has been tested on GPUs with compute compatibility 8.0 on DGX A100 machine.
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.
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.
The physical design flow requires RTL, Python, and Tcl files from the TILOS-MacroPlacement repository. Only the codes that we have added and modified are provided in scripts.
