Generative adversarial network based scalable on-chip noise sensor placement

Liu, Jinglan; Ding, Yan; Yang, Jianlei; Schlichtmann, Ulf; Shi, Yiyu

doi:10.1109/socc.2017.8226048

Cited by 8 publications

(4 citation statements)

References 19 publications

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“…This RL-based approach outperforms state-of-the-art baselines and the produced results comparable to manual designs from experts. Liu et al [34] used GAN to create noise maps from limited samples. These noise maps are fed into an optimization algorithm to find a placement for noise sensors.…”

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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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.

View full text Add to dashboard Cite

show abstract

“…Recently GAN-based methods have been applied for VLSI physical designs such as generation of the various noise maps to facility the IR-drop noise sensor placement [38], for layout lithography analysis [39] and sub-resolution assist feature generation [40], for analog layout well generation [41]. But less studies have been investigated for data-driven circuit level and thermal analysis to model the dynamic systems described by the partial differential equations.…”

Section: Related Workmentioning

confidence: 99%

Full-chip thermal map estimation for commercial multi-core CPUs with generative adversarial learning

Jin

Sadiqbatcha

Zhang

et al. 2020

Proceedings of the 39th International Conference on Computer-Aided Design

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“…Recently, generative adversarial networks (GAN) [16] gained much traction as it can learn features (latent representation) without extensively annotated training data. GAN-based methods have been applied for VLSI physical designs such as generation of various noise maps to facilitate the IR-drop noise sensor placement [22], for layout lithography analysis [30] and sub-resolution assist feature generation [4], for analog layout well generation [29]. However, the proposed GAN-based design and analysis techniques are mainly targeted for the statistical and static image generations (analysis).…”

Section: Introductionmentioning

confidence: 99%