Stochastic extended Krylov subspace method for variational analysis of on-chip power grid networks
Proceedings of the 2007 IEEE/ACM 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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This paper describes the stochastic model order reduction algorithm via stochastic Hermite polynomials from the practical implementation perspective. Comparing with existing work on stochastic interconnect analysis and parameterized model order reduction, we generalized the input variation representation using polynomial chaos (PC) to allow for accurate modeling of non-Gaussian input variations. We also explore the implicit system representation using sub-matrices and improved the efficiency for solving the linear equations utilizing block matrix structure of the augmented system. Experiments show that our algorithm matches with Monte Carlo methods very well while keeping the algorithm effective. And the PC representation of non-Gaussian variables gains more accuracy than Taylor representation used in previous work (Wang et al., 2004).