1. [Publications](/index.php/publications)
2. TransSizer: A Novel Transformer-Based Fast Gate Sizer
 
 # TransSizer: A Novel Transformer-Based Fast Gate Sizer

  ![](/sites/default/files/styles/wide/public/publications/TransSizer.JPG?itok=lIkUzfuU)

 Gate sizing is a fundamental netlist optimization move and researchers have used supervised learning-based models in gate sizers. Recently, Reinforcement Learning (RL) has been tried for sizing gates (and other EDA optimization problems) but are very runtime-intensive. In this work, we explore a novel Transformer-based gate sizer, *TransSizer*, to *directly generate optimized* gate sizes given a placed and unoptimized netlist. TransSizer is trained on datasets obtained from real tapeout-quality industrial designs in a foundry 5*nm* technology node. Our results indicate that TransSizer achieves 97% accuracy in predicting optimized gate sizes at the postroute optimization stage. Furthermore, TransSizer has a speedup of ~1400× while delivering similar timing, power and area metrics when compared to a leading-edge commercial tool for sizing-only optimization.



 ## Authors



Siddhartha Nath (NVIDIA)

Geraldo Pradipta (NVIDIA)

Corey Hu (NVIDIA)

Tian Yang (NVIDIA)

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

Mark Haoxing Ren (NVIDIA)

 

 

 ## Publication Date



Sunday, October 30, 2022

 

 ## Published in



[2022 International Conference on Computer-Aided Design](https://iccad.com/)

 

 ## 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



[Paper](https://dl.acm.org/doi/abs/10.1145/3508352.3549442)

 

 

 ## 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/>.