From Finance to Flip Flops: A Study of Fast Quasi-Monte Carlo Methods from Computational Finance Applied to Statistical Circuit Analysis

Author

Singhee, Amith ; Rutenbar, Rob A.

Author_Institution

Dept. of ECE, Carnegie Mellon Univ., Pittsburgh, PA

fYear

2007

fDate

26-28 March 2007

Firstpage

685

Lastpage

692

Abstract

Problems in computational finance share many of the characteristics that challenge us in statistical circuit analysis: high dimensionality, profound nonlinearity, stringent accuracy requirements, and expensive sample simulation. We offer a detailed experimental study of how one celebrated technique from this domain - quasi-Monte Carlo (QMC) analysis - can be used for fast statistical circuit analysis. In contrast with traditional pseudo-random Monte Carlo sampling, QMC substitutes a (shorter) sequence of deterministically chosen sample points. Across a set of digital and analog circuits, in 90nm and 45nm technologies, varying in size from 30 to 400 devices, we obtain speedups in parametric yield estimation from 2times to 50times

Keywords

Monte Carlo methods; circuit analysis computing; sampling methods; 45 nm; 90 nm; QMC analysis; analog circuits; computational finance; digital circuits; flip flops; parametric yield estimation; pseudo-random Monte Carlo sampling; quasi-Monte Carlo methods; statistical circuit analysis; Analytical models; Circuit analysis; Circuit analysis computing; Computational modeling; Convergence; Finance; Instruments; Monte Carlo methods; Pricing; Security;

fLanguage

English

Publisher

ieee

Conference_Titel

Quality Electronic Design, 2007. ISQED '07. 8th International Symposium on

Conference_Location

San Jose, CA

Print_ISBN

0-7695-2795-7

Type

conf

DOI

10.1109/ISQED.2007.79

Filename

4149114