Elements of information theory
Elements of information theory
Parameter variations and impact on circuits and microarchitecture
Proceedings of the 40th annual Design Automation Conference
First-order incremental block-based statistical timing analysis
Proceedings of the 41st annual Design Automation Conference
Robust extraction of spatial correlation
Proceedings of the 2006 international symposium on Physical design
FPGA device and architecture evaluation considering process variations
ICCAD '05 Proceedings of the 2005 IEEE/ACM International conference on Computer-aided design
Proceedings of the 43rd annual Design Automation Conference
An Introduction to Copulas (Springer Series in Statistics)
An Introduction to Copulas (Springer Series in Statistics)
A framework for statistical timing analysis using non-linear delay and slew models
Proceedings of the 2006 IEEE/ACM international conference on Computer-aided design
An accurate sparse matrix based framework for statistical static timing analysis
Proceedings of the 2006 IEEE/ACM international conference on Computer-aided design
Interactive presentation: Statistical dual-Vdd assignment for FPGA interconnect power reduction
Proceedings of the conference on Design, automation and test in Europe
Non-linear statistical static timing analysis for non-Gaussian variation sources
Proceedings of the 44th annual Design Automation Conference
Proceedings of the 16th international ACM/SIGDA symposium on Field programmable gate arrays
Non-Gaussian statistical timing analysis using second-order polynomial fitting
Proceedings of the 2008 Asia and South Pacific Design Automation Conference
Modeling Objects with Local Descriptors of Biologically Motivated Selective Attention
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 05
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Majority of practical multivariate statistical analyses and optimizations model interdependence among random variables in terms of the linear correlation among them. Though linear correlation is simple to use and evaluate, in several cases non-linear dependence between random variables may be too strong to ignore. In this paper, We propose polynomial correlation coefficients as simple measure of multi-variable non-linear dependence and show that need for modeling non-linear dependence strongly depends on the end function that is to be evaluated from the random variables. Then, we calculate the errors in estimation which result from assuming independence of components generated by linear de-correlation techniques such as PCA and ICA. The experimental result shows that the error predicted by our method is within 1% error compared to the real simulation. In order to deal with non-linear dependence, we further develop a target function driven component analysis algorithm (FCA) to minimize the error caused by ignoring high order dependence and apply such technique to statistical leakage power analysis and SRAM cell noise margin variation analysis. Experimental results show that the proposed FCA method is more accurate compared to the traditional PCA or ICA.