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Novel Algorithms for Fast Statistical Analysis of Scaled Circuits de Amith Singhee

Novel Algorithms for Fast Statistical Analysis of Scaled Circuits: Lecture Notes in Electrical Engineering, cartea 46

Autor Amith Singhee, Rob A. Rutenbar
en Limba Engleză Paperback – 7 mar 2012
As VLSI technology moves to the nanometer scale for transistor feature sizes, the impact of manufacturing imperfections result in large variations in the circuit performance. Traditional CAD tools are not well-equipped to handle this scenario, since they do not model this statistical nature of the circuit parameters and performances, or if they do, the existing techniques tend to be over-simplified or intractably slow. Novel Algorithms for Fast Statistical Analysis of Scaled Circuits draws upon ideas for attacking parallel problems in other technical fields, such as computational finance, machine learning and actuarial risk, and synthesizes them with innovative attacks for the problem domain of integrated circuits. The result is a set of novel solutions to problems of efficient statistical analysis of circuits in the nanometer regime.
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Specificații

ISBN-13: 9789400736870
ISBN-10: 9400736878
Pagini: 212
Ilustrații: XV, 195 p.
Dimensiuni: 155 x 235 x 11 mm
Greutate: 0.3 kg
Ediția:2009
Editura: SPRINGER NETHERLANDS
Colecția Springer
Seria Lecture Notes in Electrical Engineering

Locul publicării:Dordrecht, Netherlands

Public țintă

Professional/practitioner

Cuprins

SiLVR: Projection Pursuit for Response Surface Modeling.- Quasi-Monte Carlo for Fast Statistical Simulation of Circuits.- Statistical Blockade: Estimating Rare Event Statistics.- Concluding Observations.

Recenzii

The Statistical Blockade method proposed by Singhee and Rutenbar will make a significant impact on the design of next-generation digital integrated circuits. It has the potential to dramatically reduce simulation time compared to a traditional Monte Carlo approach. Their award winning work is well received by industry and has influenced research directions in academia.
- Prof. Anantha Chandrakasan, MIT

Textul de pe ultima copertă

As VLSI technology moves to the nanometer scale for transistor feature sizes, the impact of manufacturing imperfections result in large variations in the circuit performance. Traditional CAD tools are not well-equipped to handle this scenario, since they do not model this statistical nature of the circuit parameters and performances, or if they do, the existing techniques tend to be over-simplified or intractably slow. Novel Algorithms for Fast Statistical Analysis of Scaled Circuits draws upon ideas for attacking parallel problems in other technical fields, such as computational finance, machine learning and actuarial risk, and synthesizes them with innovative attacks for the problem domain of integrated circuits. The result is a set of novel solutions to problems of efficient statistical analysis of circuits in the nanometer regime. In particular, Novel Algorithms for Fast Statistical Analysis of Scaled Circuits makes three contributions:
1) SiLVR, a nonlinear response surface modeling and performance-driven dimensionality reduction strategy, that automatically captures the designer’s insight into the circuit behavior, by extracting quantitative measures of relative global sensitivities and nonlinear correlation.
2) Fast Monte Carlo simulation of circuits using quasi-Monte Carlo, showing speedups of 2× to 50× over standard Monte Carlo.
3) Statistical blockade, an efficient method for sampling rare events and estimating their probability distribution using limit results from extreme value theory, applied to high replication circuits like SRAM cells.

Caracteristici

Presents flexible and general techniques for statistical analysis that can be applied to wide variety of circuit applications Applies theory from a wide variety of scientific fields (machine learning, computational finance, number theory, actuarial studies) Covers relevant theory in detail This is the first book to present these novel techniques Extensive experiments, illustrative examples and analysis Includes supplementary material: sn.pub/extras