rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Novel Algorithms for Fast Statistical Analysis of Scaled Circuits
  name:
    name part: Singhee, Amith.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Rutenbar, Rob A.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: ne
    date issued: 2009
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XV, 195 p. online resource.
  abstract: 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.
  table of contents: SiLVR: Projection Pursuit for Response Surface Modeling -- Quasi-Monte Carlo for Fast Statistical Simulation of Circuits -- Statistical Blockade: Estimating Rare Event Statistics -- Concluding Observations.
  note: by Amith Singhee, Rob A. Rutenbar.
  subject:
    topic: Engineering
  subject:
    topic: Computer system failures
  subject:
    topic: Data structures (Computer science)
  subject:
    topic: Electronic circuits
  subject:
    topic: Engineering
  subject:
    topic: Circuits and Systems
  subject:
    topic: Data Structures
  subject:
    topic: System Performance and Evaluation
  classification: TK7888.4
  classification: 621.3815
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Lecture Notes in Electrical Engineering, 46
  identifier: 9789048131006
  identifier: http://dx.doi.org/10.1007/978-90-481-3100-6
  record info:
    record creation date: 100301
    record change date: 20151204181827.0
    record identifier: 978-90-481-3100-6