Learning predictors for flash memory endurance: a comparative study of alternative classification methods
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{Arbuckle:2014:IJCISTUDIES,
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author = "Tom Arbuckle and Damien Hogan and Conor Ryan",
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title = "Learning predictors for flash memory endurance: a
comparative study of alternative classification
methods",
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journal = "International Journal of Computational Intelligence
Studies",
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year = "2014",
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volume = "3",
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number = "1",
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pages = "18--39",
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month = jan # "~14",
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keywords = "genetic algorithms, genetic programming, flash memory
endurance, performance prediction, linear programming,
support vector machines, SVMs, learning predictors,
classification methods, timing data, erasure,
programming, modelling",
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publisher = "Inderscience Publishers",
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language = "eng",
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ISSN = "1755-4985",
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bibsource = "OAI-PMH server at www.inderscience.com",
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URL = "http://www.inderscience.com/link.php?id=58644",
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DOI = "doi:10.1504/IJCISTUDIES.2014.058644",
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abstract = "Flash memory's ability to be programmed multiple times
is called its endurance. Beyond being able to give more
accurate chip specifications, more precise knowledge of
endurance would permit manufacturers to use flash chips
more effectively. Rather than physical testing to
determine chip endurance, which is impractical because
it takes days and destroys an area of the chip under
test, this research seeks to predict whether chips will
meet chosen endurance criteria. Timing data relating to
erasure and programming operations is gathered as the
basis for modelling. The purpose of this paper is to
determine which methods can be used on this data to
accurately and efficiently predict endurance.
Traditional statistical classification methods, support
vector machines and genetic programming are compared.
Cross-validating on common datasets, the classification
methods are evaluated for applicability, accuracy and
efficiency and their respective advantages and
disadvantages are quantified.",
- }
Genetic Programming entries for
Tom Arbuckle
Damien Hogan
Conor Ryan
Citations