Novel Algorithms for Fast Statistical Analysis of Scaled Circuits
Language: English
Published by Springer, 2012
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Seller Inventory # 9789400736870
- Title
- Novel Algorithms for Fast Statistical Analysis of Scaled Circuits
- Author
- Amith Singhee
- Publisher
- Springer
- Publication year
- 2012
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 9400736878
- ISBN 13
- 9789400736870
- Item weight
- 330 grams
- Dimensions
- 235x155x12 mm
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.
"Synopsis" may belong to another edition of this title.
From the Back Cover
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.
"About the title" may belong to another edition of this title.
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