research-article

Heterogeneous Graph Neural Network-Based Imitation Learning for Gate Sizing Acceleration

Authors:

Guangliang Zhang,

Guangyong Chen,

Pheng Ann HengAuthors Info & Claims

ICCAD '22: Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design

Article No.: 57, Pages 1 - 9

https://doi.org/10.1145/3508352.3549361

Published: 22 December 2022 Publication History

Abstract

Gate Sizing is an important step in logic synthesis, where the cells are resized to optimize metrics such as area, timing, power, leakage, etc. In this work, we consider the gate sizing problem for leakage power optimization with timing constraints. Lagrangian Relaxation is a widely employed optimization method for gate sizing problems. We accelerate Lagrangian Relaxation-based algorithms by narrowing down the range of cells to resize. In particular, we formulate a heterogeneous directed graph to represent the timing graph, propose a heterogeneous graph neural network as the encoder, and train in the way of imitation learning to mimic the selection behavior of each iteration in Lagrangian Relaxation. This network is used to predict the set of cells that need to be changed during the optimization process of Lagrangian Relaxation. Experiments show that our accelerated gate sizer could achieve comparable performance to the baseline with an average of 22.5% runtime reduction.

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Cited By

Jiang WChhabria VSapatnekar SAmrouch HHu JGarg SLin Y(2024)IR-Aware ECO Timing Optimization Using Reinforcement LearningProceedings of the 2024 ACM/IEEE International Symposium on Machine Learning for CAD10.1145/3670474.3685945(1-7)Online publication date: 9-Sep-2024
https://dl.acm.org/doi/10.1145/3670474.3685945
Jiang WChhabria VSapatnekar S(2024)IR-Aware ECO Timing Optimization Using Reinforcement Learning2024 ACM/IEEE 6th Symposium on Machine Learning for CAD (MLCAD)10.1109/MLCAD62225.2024.10740227(1-7)Online publication date: 9-Sep-2024
https://doi.org/10.1109/MLCAD62225.2024.10740227
He GDing WYe YCheng XSong QCao PKim T(2024)An Optimization-Aware Pre-Routing Timing Prediction Framework Based on Heterogeneous Graph LearningProceedings of the 29th Asia and South Pacific Design Automation Conference10.1109/ASP-DAC58780.2024.10473937(177-182)Online publication date: 22-Jan-2024
https://dl.acm.org/doi/10.1109/ASP-DAC58780.2024.10473937
Show More Cited By

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Heterogeneous Graph Neural Network-Based Imitation Learning for Gate Sizing Acceleration
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cover image ACM Conferences

ICCAD '22: Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design

October 2022

1467 pages

ISBN:9781450392174

DOI:10.1145/3508352

Conference Chair:
Tulika Mitra
National University of Singapore
,
Program Chairs:
Evangeline Young
The Chinese University of Hong Kong
,
Jinjun Xiong
University at Buffalo (UB)

Copyright © 2022 ACM.

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New York, NY, United States

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Published: 22 December 2022

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ICCAD '22

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ICCAD '22: IEEE/ACM International Conference on Computer-Aided Design

October 30 - November 3, 2022

California, San Diego

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Cited By

Jiang WChhabria VSapatnekar SAmrouch HHu JGarg SLin Y(2024)IR-Aware ECO Timing Optimization Using Reinforcement LearningProceedings of the 2024 ACM/IEEE International Symposium on Machine Learning for CAD10.1145/3670474.3685945(1-7)Online publication date: 9-Sep-2024
https://dl.acm.org/doi/10.1145/3670474.3685945
Jiang WChhabria VSapatnekar S(2024)IR-Aware ECO Timing Optimization Using Reinforcement Learning2024 ACM/IEEE 6th Symposium on Machine Learning for CAD (MLCAD)10.1109/MLCAD62225.2024.10740227(1-7)Online publication date: 9-Sep-2024
https://doi.org/10.1109/MLCAD62225.2024.10740227
He GDing WYe YCheng XSong QCao PKim T(2024)An Optimization-Aware Pre-Routing Timing Prediction Framework Based on Heterogeneous Graph LearningProceedings of the 29th Asia and South Pacific Design Automation Conference10.1109/ASP-DAC58780.2024.10473937(177-182)Online publication date: 22-Jan-2024
https://dl.acm.org/doi/10.1109/ASP-DAC58780.2024.10473937
Alrahis LKnechtel JSinanoglu OTakahashi A(2023)Graph Neural NetworksProceedings of the 28th Asia and South Pacific Design Automation Conference10.1145/3566097.3568345(83-90)Online publication date: 16-Jan-2023
https://dl.acm.org/doi/10.1145/3566097.3568345

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