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2. AutoDMP: Automated DREAMPlace-based Macro Placement
 
 # AutoDMP: Automated DREAMPlace-based Macro Placement

  ![](/sites/default/files/styles/wide/public/publications/autodmp-figure.png?itok=_h2XJUH2)

 Macro placement is a critical very large-scale integration (VLSI) physical design problem that significantly impacts the design powerperformance-area (PPA) metrics. This paper proposes AutoDMP, a methodology that leverages DREAMPlace, a GPU-accelerated placer, to place macros and standard cells concurrently in conjunction with automated parameter tuning using a multi-objective hyperparameter optimization technique. As a result, we can generate high-quality predictable solutions, improving the macro placement quality of academic benchmarks compared to baseline results generated from academic and commercial tools. AutoDMP is also computationally efficient, optimizing a design with 2.7 million cells and 320 macros in 3 hours on a single NVIDIA DGX Station A100. This work demonstrates the promise and potential of combining GPU-accelerated algorithms and ML techniques for VLSI design automation



 ## Authors



Anthony Agnesina (NVIDIA)

Puranjay Rajvanshi (NVIDIA)

Tian Yang (NVIDIA)

Geraldo Pradipta (NVIDIA)

Austin Jiao (NVIDIA)

[Ben Keller](/index.php/person/ben-keller)

[Brucek Khailany](/index.php/person/brucek-khailany)

Haoxing (Mark) Ren (NVIDIA)

 

 

 ## Publication Date



Sunday, March 26, 2023

 

 ## Published in



[International Symposium on Physical Design 2023](https://ispd.cc/ispd2023/index.php)

 

 ## Research Area



[Artificial Intelligence and Machine Learning ](/index.php/research-area/machine-learning-artificial-intelligence)

[Circuits and VLSI Design](/index.php/research-area/circuits)

 

 

 ## External Links



[GitHub repo](https://github.com/NVlabs/AutoDMP)

 

 

 ## Uploaded Files



[paper](https://d1qx31qr3h6wln.cloudfront.net/publications/AutoDMP.pdf "Open file in new window")5.82 MB

 

 

 ## Copyright



Copyright by the Association for Computing Machinery, Inc. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept, ACM Inc., fax +1 (212) 869-0481, or <permissions@acm.org>. The definitive version of this paper can be found at ACM's Digital Library <http://www.acm.org/dl/>.