TY - JOUR
T1 - Statistical timing and power analysis of VLSI considering non-linear dependence
AU - Cheng, Lerong
AU - Xu, Wenyao
AU - Ren, Fengbo
AU - Gong, Fang
AU - Gupta, Puneet
AU - He, Lei
N1 - Funding Information:
Puneet Gupta received the B.Tech. degree in electrical engineering from the Indian Institute of Technology Delhi, New Delhi, India, in 2000, and the Ph.D. degree from the University of California at San Diego (UCSD), San Diego, in 2007. He co-founded Blaze DFM, Inc., Sunnyvale, CA (acquired by Tela, Inc.) in 2004 and was its Product Architect until 2007. He is currently a Faculty Member with the Department of Electrical Engineering, UCLA. He has authored over 80 papers, ten U.S. patents, and a book chapter. His current research interests include building high-value bridges across application–architecture–implementation–fabrication interfaces for loweredcost and power, increased yield, and improved predictability of integrated circuits and systems. Dr. Gupta was a recipient of the NSF CAREER Award, the ACM/SIGDA Outstanding New Faculty Award, the SRC Inventor Recognition Award, the European Design Automation Association Outstanding Dissertation Award, and the IBM Ph.D. Fellowship. He has given tutorial talks at DAC, ICCAD, the International VLSI Design Conference, and the SPIE Advanced Lithography Symposium. He was the Technical Program Committee Member of DAC, ICCAD, ASPDAC, ISQED, ICCD, SLIP, and VLSI Design. He wasthe Program Chair of the IEEE DFM&Y Workshop in 2009 and 2010.
PY - 2014/9
Y1 - 2014/9
N2 - Majority of practical multivariate statistical analysis and optimizations model interdependence among random variables in terms of the linear correlation. 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 the 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 resulting from assuming independence of components generated by linear de-correlation techniques, such as PCA and ICA. The experimental results show that the error predicted by our method is within 1% error compared to the real simulation of statistical timing and leakage analysis. 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. We apply FCA 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.
AB - Majority of practical multivariate statistical analysis and optimizations model interdependence among random variables in terms of the linear correlation. 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 the 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 resulting from assuming independence of components generated by linear de-correlation techniques, such as PCA and ICA. The experimental results show that the error predicted by our method is within 1% error compared to the real simulation of statistical timing and leakage analysis. 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. We apply FCA 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.
KW - Statistical modeling
KW - VLSI
KW - Yield analysis
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U2 - 10.1016/j.vlsi.2013.12.004
DO - 10.1016/j.vlsi.2013.12.004
M3 - Article
AN - SCOPUS:84903278014
SN - 0167-9260
VL - 47
SP - 487
EP - 498
JO - Integration, the VLSI Journal
JF - Integration, the VLSI Journal
IS - 4
ER -