High-dimensional statistical modeling and analysis of custom integrated circuits

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@InProceedings{McConaghy:2011:CICC,
  author =       "Trent McConaghy",
  title =        "High-dimensional statistical modeling and analysis of
                 custom integrated circuits",
  booktitle =    "Proceedings of the IEEE Custom Integrated Circuits
                 Conference (CICC 2011)",
  year =         "2011",
  address =      "San Jose, CA, USA",
  month =        "19-21 " # sep,
  note =         "invited paper",
  keywords =     "genetic algorithms, genetic programming, integrated
                 circuit design, integrated circuit modelling,
                 statistical analysis, SPICE, compact equation
                 extraction, custom circuit designers, custom integrated
                 circuits, deterministic technique, high-dimensional
                 statistical modelling, integrated circuit modelling
                 problems, manual equation-based approach, Complexity
                 theory, Equations, Integrated circuit modelling,
                 Learning systems, Mathematical model, Niobium,
                 Predictive models",
  isbn13 =       "978-1-4577-0222-8",
  ISSN =         "0886-5930",
  URL =          "http://trent.st/content/2011-CICC-FFX-paper.pdf",
  slide_url =    "http://trent.st/content/2011-CICC-FFX-slides.ppt",
  DOI =          "doi:10.1109/CICC.2011.6055329",
  size =         "8 pages",
  abstract =     "Custom circuit designers have long favoured manual
                 equation-based approaches in early design stages,
                 because it gives excellent insight and control over the
                 design. However, this flow is threatened: as modern
                 process nodes advance, process variation affects
                 circuit performance more strongly, hurting the accuracy
                 of existing equations. Because designers are typically
                 not statistical modeling experts, it is difficult to
                 adapt the equations to incorporate statistical
                 variations. This paper presents a fast, deterministic
                 technique to help designers revise equations to account
                 for statistical variation. Specifically, the technique
                 extracts compact equations of performance as a function
                 of process variables, even for cases when there are
                 thousands of possible variables and the equations are
                 highly nonlinear. In fact, it provides a whole set of
                 equations that trade off simplicity versus accuracy
                 compared to SPICE. The technique is validated on a
                 broad range of custom integrated circuit modeling
                 problems.",
  notes =        "also known as \cite{6055329}",
}

Genetic Programming entries for Trent McConaghy

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