ICCAD '97 Proceedings of the 1997 IEEE/ACM international conference on Computer-aided design
Modeling Interconnect Variability Using Efficient Parametric Model Order Reduction
Proceedings of the conference on Design, Automation and Test in Europe - Volume 2
Variational interconnect analysis via PMTBR
Proceedings of the 2004 IEEE/ACM International conference on Computer-aided design
Random sampling of moment graph: a stochastic Krylov-reduction algorithm
Proceedings of the conference on Design, automation and test in Europe
ARMS - automatic residue-minimization based sampling for multi-point modeling techniques
Proceedings of the 46th Annual Design Automation Conference
PRIMA: passive reduced-order interconnect macromodeling algorithm
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Poor man's TBR: a simple model reduction scheme
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Resampling Plans for Sample Point Selection in Multipoint Model-Order Reduction
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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This paper describes a Model Order Reduction algorithm for multi-dimensional parameterized systems, based on a sampling procedure which incorporates a low order moment matching paradigm into a multi-point based methodology. The procedure seeks to maximize the subspace generated by a given number of samples, selected among an initial candidate set. The selection is based on a global criteria that chooses the sample whose associated vector adds more information to the existing subspace. However, the initial candidate set can be extremely large for high-dimensional systems, and thus the procedure can be costly. To improve efficiency we propose a scheme to incorporate information from low order moments to the basis with small extra cost, in order to extend the approximation to a wider region around the selected point. This will allow reduction of the initial candidate set without decreasing the level of confidence. We further improve the procedure by generating the global subspace based on the composition of local approximations. To achieve this, the initial candidates will be split into subsets that will be considered as independent regions, and in a first phase the procedure applied locally thus enabling improved efficiency and providing a framework for almost perfect parallelization.