Model Reduction and Simulation of Nonlinear Circuits via Tensor Decomposition
Name
TNMOR_tcad15.pdf
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4 MB
Format
Adobe PDF
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d1c11a578fe48188a827bd1622391d1b
Author(s)
Haotian Liu
Daniel, Luca
Ngai Wong, Luca
Date Issued
March 2015
Journal
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Haotian Liu, Luca Daniel, and Ngai Wong. “Model Reduction and Simulation of Nonlinear Circuits via Tensor Decomposition.” IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst. 34, no. 7 (July 2015): 1059–1069.
Version
Author's final manuscript
Abstract
Model order reduction of nonlinear circuits (especially highly nonlinear circuits) has always been a theoretically and numerically challenging task. In this paper, we utilize tensors (namely, a higher order generalization of matrices) to develop a tensor-based nonlinear model order reduction algorithm we named TNMOR for the efficient simulation of nonlinear circuits. Unlike existing nonlinear model order reduction methods, in TNMOR high-order nonlinearities are captured using tensors, followed by decomposition and reduction to a compact tensor-based reduced-order model. Therefore, TNMOR completely avoids the dense reduced-order system matrices, which in turn allows faster simulation and a smaller memory requirement if relatively low-rank approximations of these tensors exist. Numerical experiments on transient and periodic steady-state analyses confirm the superior accuracy and efficiency of TNMOR, particularly in highly nonlinear scenarios.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/TCAD.2015.2409272
