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Please use this identifier to cite or link to this item: http://hdl.handle.net/2005/2774

Title: Clustering for Model Reduction of Circuits : Multi-level Techniques
Authors: Milind, R
Advisors: Raha, Soumyendu
Keywords: MOR
Model Order Reduction
Clustering based Model Reduction
Model Order Reduction Algorithms
PRIMA Clustering Model Reduction
Linear Circuits -
Electronic Circuits
Krylov-subspace Methods
Model Reduction
Submitted Date: 2014
Series/Report no.: G26303
Abstract: Miniaturisation of electronic chips poses challenges at the design stage. The progressively decreasing circuit dimensions result in complex electrical behaviour that necessitates complex models. Simulation of complex circuit models involves extraordinarily large compu- tational complexity. Such complexity is better managed through Model Order Reduction. Model order reduction has been successful in large reductions in system order for most types of circuits, at high levels of accuracy. However, multiport circuits with large number of inputs/outputs, pose an additional computational challenge. A strategy based on exible clustering of interconnects results in more e cient reduction of multiport circuits. Clustering methods traditionally use Krylov-subspace methods such as PRIMA for the actual model reduction step. These clustering methods are unable to reduce the model order to the optimum extent. SVD-based methods like Truncated Balanced Realization have shown higher reduction potential than Krylov-subspace methods. In this thesis, the di erences in reduction potential and computational cost thereof between SVD-based methods and Krylov-subspace methods are identi ed, analyzed and quanti ed. A novel algorithm has been developed, utilizing a particular combination of both these methods to achieve better results. It enhances the clustering method for model reduction using Truncated Balanced Realization as a second-level reduction technique. The algorithm is tested and signi cant gains are illustrated. The proposed novel algorithm preserves the other advantages of the current clustering algorithm.
Abstract file URL: http://etd.ncsi.iisc.ernet.in/abstracts/3634/G26303-Abs.pdf
URI: http://hdl.handle.net/2005/2774
Appears in Collections:Supercomputer Education and Research Centre (serc)

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