Learning predictors for flash memory endurance: a comparative study of alternative classification methods

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Tom Arbuckle and Damien Hogan and Conor Ryan",
  title =        "Learning predictors for flash memory endurance: a
                 comparative study of alternative classification
  journal =      "International Journal of Computational Intelligence
  year =         "2014",
  volume =       "3",
  number =       "1",
  pages =        "18--39",
  month =        jan # "~14",
  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",
  publisher =    "Inderscience Publishers",
  language =     "eng",
  ISSN =         "1755-4985",
  bibsource =    "OAI-PMH server at www.inderscience.com",
  URL =          "http://www.inderscience.com/link.php?id=58644",
  DOI =          "doi:10.1504/IJCISTUDIES.2014.058644",
  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