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

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 2X to 50X.

• Type: Conference
• Year: 2007
• Where: ISQED
• Authors: Amith Singhee, Rob A. Rutenbar
• Keywords: Expensive Sample Simulation, Hardware, ISQED 2007, Statistical Circuit Analysis, Stringent Accuracy Requirements