GAN-CTS: A Generative Adversarial Framework for Clock Tree Prediction and Optimization

Lu, Yi‐Chen; Lee, Jee Hyun; Agnesina, Anthony; Samadi, Kambiz; Lim, Sung Kyu

doi:10.1109/iccad45719.2019.8942063

Cited by 46 publications

(7 citation statements)

References 14 publications

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“…Third, an adversarial learning generator to optimize and classify CTS through strategy gradient reinforcement learning. Fourth, an adversarial learning monitor using a previously trained regression model [7]. The specific process from feature extraction to the establishment of an adversarial learning model to predict CTS outcome and success is shown in Figure 1.…”

Section: Machine Learning For Overall Optimization Of Ctsmentioning

confidence: 99%

Machine learning in physical design

Yu,

Li,

Liu

et al. 2023

TNS

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Machine learning is a highly effective instrument in constructing models that can expeditiously produce accurate prognostications. As the complexity of integrated circuit design continues to increase and process nodes continue to evolve, and physical design faces more challenges from modeling and optimization. To address these challenges, machine learning has been introduced into physical design. Thus, in this paper, we discuss the application of machine learning in physical design, covering topics such as Clock Tree Synthesis (CTS), Placement and Routing, IR-Drop and Static Timing Analysis (STA). The essay explores how machine learning can be used to overcome challenges in these areas, such as reducing peak current and clock skew in CTS, optimizing placement parameters and decision-making, predicting routability and reducing IR-drop effects. This paper also discusses various machine learning techniques (ML), such as reinforcement learning, convolutional neural networks and transfer learning. To conclude, we provide insights into how machine learning can be applied to improve various aspects of physical design.

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Section: Machine Learning For Overall Optimization Of Ctsmentioning

confidence: 99%

Machine learning in physical design

Yu,

Li,

Liu

et al. 2023

TNS

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“…Lu et al [46] proposed GAN-CTS, which employs GAN and RL for clock tree prediction. They take flip flop distribution, clock net distribution, and trial routing results as input images.…”

Section: Physical Designmentioning

confidence: 99%

MLCAD: A Survey of Research in Machine Learning for CAD Keynote Paper

Rapp

Amrouch

Lin

et al. 2022

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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Due to the increasing size of integrated circuits (ICs), their design and optimization phases (i.e., computer-aided design, CAD) grow increasingly complex. At design time, a large design space needs to be explored to find an implementation that fulfills all specifications and then optimizes metrics like energy, area, delay, reliability, etc. At run time, a large configuration space needs to be searched to find the best set of parameters (e.g., voltage/frequency) to further optimize the system. Both spaces are infeasible for exhaustive search typically leading to heuristic optimization algorithms that find some tradeoff between design quality and computational overhead. Machine learning (ML) can build powerful models that have successfully been employed in related domains. In this survey, we categorize how ML may be used and is used for design-time and run-time optimization and exploration strategies of ICs. A metastudy of published techniques unveils areas in computer-aided design (CAD) that are well explored and underexplored with ML, as well as trends in the employed ML algorithms. We present a comprehensive categorization and summary of the state of the art on ML for CAD. Finally, we summarize the remaining challenges and promising open research directions.

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“…Lu et al [99] employ GAN and RL for clock tree prediction. Flip flop distribution, clock net distribution, and trial routing results serve as input images.…”

Section: Routing Information Predictionmentioning

confidence: 99%

Machine Learning for Electronic Design Automation: A Survey

Huang

et al. 2021

Preprint

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With the down-scaling of CMOS technology, the design complexity of very large-scale integrated (VLSI) is increasing. Although the application of machine learning (ML) techniques in electronic design automation (EDA) can trace its history back to the 90s, the recent breakthrough of ML and the increasing complexity of EDA tasks have aroused more interests in incorporating ML to solve EDA tasks. In this paper, we present a comprehensive review of existing ML for EDA studies, organized following the EDA hierarchy.

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GAN-CTS: A Generative Adversarial Framework for Clock Tree Prediction and Optimization

Cited by 46 publications

References 14 publications

Machine learning in physical design

Machine learning in physical design

MLCAD: A Survey of Research in Machine Learning for CAD Keynote Paper

Machine Learning for Electronic Design Automation: A Survey

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