A Global Bayesian Optimization Algorithm and Its Application to Integrated System Design
Increasing levels of system integration pose difficulties in meeting design specifications for high-performance systems. Oftentimes increased complexity, nonlinearity, and multiple tradeoffs need to be handled simultaneously during the design cycle. Since components in such systems are highly correl...
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| Published in: | IEEE transactions on very large scale integration (VLSI) systems Vol. 26; no. 4; pp. 792 - 802 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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IEEE
01.04.2018
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| ISSN: | 1063-8210, 1557-9999 |
| Online Access: | Get full text |
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| Abstract | Increasing levels of system integration pose difficulties in meeting design specifications for high-performance systems. Oftentimes increased complexity, nonlinearity, and multiple tradeoffs need to be handled simultaneously during the design cycle. Since components in such systems are highly correlated with each other, codesign and co-optimization of the complete system are required. Machine learning (ML) provides opportunities for analyzing such systems with multiple control parameters, where techniques based on Bayesian optimization (BO) can be used to meet or exceed design specifications. In this paper, we propose a new BO-based global optimization algorithm titled Two-Stage BO (TSBO). TSBO can be applied to black box optimization problems where the computational time can be reduced through a reduction in the number of simulations required. Empirical analysis on a set of popular challenge functions with several local extrema and dimensions shows TSBO to have a faster convergence rate as compared with other optimization methods. In this paper, TSBO has been applied for clock skew minimization in 3-D integrated circuits and multiobjective co-optimization for maximizing efficiency in integrated voltage regulators. The results show that TSBO is between <inline-formula> <tex-math notation="LaTeX">2\times </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">4\times </tex-math></inline-formula> faster as compared with previously published BO algorithms and other non-ML-based techniques. |
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| AbstractList | Increasing levels of system integration pose difficulties in meeting design specifications for high-performance systems. Oftentimes increased complexity, nonlinearity, and multiple tradeoffs need to be handled simultaneously during the design cycle. Since components in such systems are highly correlated with each other, codesign and co-optimization of the complete system are required. Machine learning (ML) provides opportunities for analyzing such systems with multiple control parameters, where techniques based on Bayesian optimization (BO) can be used to meet or exceed design specifications. In this paper, we propose a new BO-based global optimization algorithm titled Two-Stage BO (TSBO). TSBO can be applied to black box optimization problems where the computational time can be reduced through a reduction in the number of simulations required. Empirical analysis on a set of popular challenge functions with several local extrema and dimensions shows TSBO to have a faster convergence rate as compared with other optimization methods. In this paper, TSBO has been applied for clock skew minimization in 3-D integrated circuits and multiobjective co-optimization for maximizing efficiency in integrated voltage regulators. The results show that TSBO is between <inline-formula> <tex-math notation="LaTeX">2\times </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">4\times </tex-math></inline-formula> faster as compared with previously published BO algorithms and other non-ML-based techniques. |
| Author | Kavungal Davis, Anto Torun, Hakki Mert Swaminathan, Madhavan Bellaredj, Mohamed Lamine Faycal |
| Author_xml | – sequence: 1 givenname: Hakki Mert orcidid: 0000-0002-9611-1658 surname: Torun fullname: Torun, Hakki Mert email: htorun3@gatech.edu organization: School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA – sequence: 2 givenname: Madhavan surname: Swaminathan fullname: Swaminathan, Madhavan organization: School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA – sequence: 3 givenname: Anto orcidid: 0000-0002-4483-2031 surname: Kavungal Davis fullname: Kavungal Davis, Anto organization: School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA – sequence: 4 givenname: Mohamed Lamine Faycal orcidid: 0000-0003-1406-1173 surname: Bellaredj fullname: Bellaredj, Mohamed Lamine Faycal organization: School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA |
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| SubjectTerms | 3-D integration Algorithm design and analysis Bayes methods Bayesian optimization (BO) black box systems Integrated circuit modeling integrated voltage regulator (IVR) machine learning (ML) magnetic core inductor Minimization Optimization thermal management Tuning |
| Title | A Global Bayesian Optimization Algorithm and Its Application to Integrated System Design |
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