A Global Bayesian Optimization Algorithm and Its Application to Integrated System Design

Hakki Mert Torun, Madhavan Swaminathan, Anto Kavungal Davis, Mohamed Lamine Faycal Bellaredj

Research output: Contribution to journalArticlepeer-review

86 Scopus citations

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 2 ×-4 × faster as compared with previously published BO algorithms and other non-ML-based techniques.

Original languageEnglish (US)
Pages (from-to)792-802
Number of pages11
JournalIEEE Transactions on Very Large Scale Integration (VLSI) Systems
Volume26
Issue number4
DOIs
StatePublished - Apr 2018

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Electrical and Electronic Engineering

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