Name
Title | ||
|---|---|---|
Enabling High-Dimensional Hierarchical Uncertainty Quantification by ANOVA and Tensor-Train Decomposition |
Abstract | ||
|---|---|---|
Hierarchical uncertainty quantification can reduce the computational cost of stochastic circuit simulation by employing spectral methods at different levels. This paper presents an efficient framework to simulate hierarchically some challenging stochastic circuits/systems that include high-dimensional subsystems. Due to the high parameter dimensionality, it is challenging to both extract surrogate models at the low level of the design hierarchy and to handle them in the high-level simulation. In this paper, we develop an efficient analysis of variance-based stochastic circuit/microelectromechanical systems simulator to efficiently extract the surrogate models at the low level. In order to avoid the curse of dimensionality, we employ tensor-train decomposition at the high level to construct the basis functions and Gauss quadrature points. As a demonstration, we verify our algorithm on a stochastic oscillator with four MEMS capacitors and 184 random parameters. This challenging example is efficiently simulated by our simulator at the cost of only 10min in MATLAB on a regular personal computer. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TCAD.2014.2369505 | IEEE Trans. on CAD of Integrated Circuits and Systems |
Keywords | DocType | Volume |
stochastic modeling and simulation,Gauss quadrature points,stochastic processes,mems simulation,computational cost,matlab,uncertainty quantification,stochastic systems,MATLAB,variance-based stochastic circuit/microelectromechanical systems simulator,analysis of variance (ANOVA),MEMS capacitors,stochastic circuit simulation,ANOVA,personal computer,generalized polynomial chaos (gPC),mems capacitors,stochastic oscillator,generalized polynomial chaos (gpc),tensor train,microelectromechanical systems (MEMS) simulation,hierarchical uncertainty quantification,microelectromechanical systems (mems) simulation,Analysis of variance (ANOVA),gauss quadrature points,generalized polynomial chaos,high-level simulation,MEMS simulation,tensor-train decomposition,anova,high dimensionality,high-dimensional hierarchical uncertainty quantification,analysis of variance (anova),high level synthesis,surrogate models,Uncertainty quantification,circuit simulation | Journal | 34 |
Issue | ISSN | Citations |
1 | 0278-0070 | 14 |
PageRank | References | Authors |
0.79 | 39 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zheng Zhang | 1 | 125 | 12.54 |
| Xiu Yang | 2 | 56 | 4.31 |
| Ivan V. Oseledets | 3 | 306 | 41.96 |
| George Em Karniadakis | 4 | 1396 | 177.42 |
| Luca Daniel | 5 | 497 | 50.96 |