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
Atomic Decomposition by Basis Pursuit
SIAM Review
Convex Optimization
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Projection-based performance modeling for inter/intra-die variations
ICCAD '05 Proceedings of the 2005 IEEE/ACM International conference on Computer-aided design
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
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
Proceedings of the 45th annual Design Automation Conference
Proceedings of the 46th Annual Design Automation Conference
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
IEEE Transactions on Information Theory
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
IEEE Transactions on Information Theory
Statistical timing analysis under spatial correlations
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
First-Order Incremental Block-Based Statistical Timing Analysis
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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
Efficient incremental analysis of on-chip power grid via sparse approximation
Proceedings of the 48th Design Automation Conference
Toward efficient spatial variation decomposition via sparse regression
Proceedings of the International Conference on Computer-Aided Design
A new uncertainty budgeting based method for robust analog/mixed-signal design
Proceedings of the 49th Annual Design Automation Conference
Proceedings of the 50th Annual Design Automation Conference
A convex macromodeling of dynamic comparator for analog circuit synthesis
Analog Integrated Circuits and Signal Processing
Proceedings of the International Conference on Computer-Aided Design
Uncertainty quantification for integrated circuits: stochastic spectral methods
Proceedings of the International Conference on Computer-Aided Design
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The aggressive scaling of integrated circuit technology results in high-dimensional, strongly-nonlinear performance variability that cannot be efficiently captured by traditional modeling techniques. In this paper, we adapt a novel L0-norm regularization method to address this modeling challenge. Our goal is to solve a large number of (e.g., 104-106) model coefficients from a small set of (e.g., 102-103) sampling points without over-fitting. This is facilitated by exploiting the underlying sparsity of model coefficients. Namely, although numerous basis functions are needed to span the high-dimensional, strongly-nonlinear variation space, only a few of them play an important role for a given performance of interest. An efficient orthogonal matching pursuit (OMP) algorithm is applied to automatically select these important basis functions based on a limited number of simulation samples. Several circuit examples designed in a commercial 65nm process demonstrate that OMP achieves up to 25× speedup compared to the traditional least-squares fitting method.