Proceedings of the 38th annual Design Automation Conference
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
An introduction to variable and feature selection
The Journal of Machine Learning Research
Convex Optimization
Projection-based performance modeling for inter/intra-die variations
ICCAD '05 Proceedings of the 2005 IEEE/ACM International conference on Computer-aided design
Design and Analysis of Experiments
Design and Analysis of Experiments
Proceedings of the 2006 IEEE/ACM international conference on Computer-aided design
Proceedings of the 44th annual Design Automation Conference
Principle Hessian direction based parameter reduction with process variation
Proceedings of the 2007 IEEE/ACM international conference on Computer-aided design
Asymptotic Probability Extraction for Nonnormal Performance Distributions
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Worst-case analysis and optimization of VLSI circuit performances
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Proceedings of the 46th Annual Design Automation Conference
Efficient design-specific worst-case corner extraction for integrated circuits
Proceedings of the 46th Annual Design Automation Conference
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Proceedings of the 47th Design Automation Conference
Generation of yield-embedded Pareto-front for simultaneous optimization of yield and performances
Proceedings of the 47th Design Automation Conference
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Stochastic analog circuit behavior modeling by point estimation method
Proceedings of the 2011 international symposium on Physical design
Efficient incremental analysis of on-chip power grid via sparse approximation
Proceedings of the 48th Design Automation Conference
An isotonic trivariate statistical regression method
Advances in Data Analysis and Classification
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The continuous technology scaling brings about high-dimensional performance variations that cannot be easily captured by the traditional response surface modeling. In this paper we propose a new statistical regression (STAR) technique that applies a novel strategy to address this high dimensionality issue. Unlike most traditional response surface modeling techniques that solve model coefficients from over-determined linear equations, STAR determines all unknown coefficients by moment matching. As such, a large number of (e.g., 103~105) model coefficients can be extracted from a small number of (e.g., 102~103) sampling points without over-fitting. In addition, a novel recursive estimator is proposed to accurately and efficiently predict the moment values. The proposed recursive estimator is facilitated by exploiting the interaction between different moment estimators and formulating the moment estimation problem into a special form that can be iteratively solved. Several circuit examples designed in commercial CMOS processes demonstrate that STAR achieves more than 20x runtime speedup compared with the traditional response surface modeling.