High-dimensional statistical modeling and analysis of custom integrated circuits

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@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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