Convex Optimization
Asymptotic probability extraction for non-normal distributions of circuit performance
Proceedings of the 2004 IEEE/ACM International conference on Computer-aided design
Projection-based performance modeling for inter/intra-die variations
ICCAD '05 Proceedings of the 2005 IEEE/ACM International conference on Computer-aided design
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
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
IEEE Transactions on Information Theory
Proceedings of the 47th Design Automation Conference
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Proceedings of the 48th Design Automation Conference
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The aggressive scaling of IC 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 L1-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 algorithm of least angle regression (LAR) 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 LAR achieves up to 25x speedup compared with the traditional least-squares fitting.