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

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

Publisher: IEEE

Abstract:

Problems in computational finance share many of the characteristics that challenge us in statistical circuit analysis: high dimensionality, profound nonlinearity, stringe...View more

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
Date of Conference: 26-28 March 2007
Date Added to IEEE Xplore: 10 April 2007
Print ISBN:0-7695-2795-7

ISSN Information:

Publisher: IEEE
Conference Location: San Jose, CA, USA

1. Introduction

Continued device scaling has dramatically increased the statistical variability with which tomorrow's circuits must contend. In a few special cases, we have analytical methods that can give us the deterministic answers we seek, e.g., optimal sizing and threshold assignment in combinational logic under statistical yield and timing constraints [1]. Unfortunately, such analytical solutions remain rare. In the general case, some combination of complex statistics, high dimensionality, profound nonlinearity or non-normality, stringent accuracy, and expensive performance evaluation (e.g., detailed simulation) thwart our analytical aspirations. What remains are the Monte Carlo methods [2].

References

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