Evolving a storage block endurance classifier for Flash memory: A trial implementation

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Hogan:2012:ieeeCIS,
  author =       "Damien Hogan and Tom Arbuckle and Conor Ryan",
  title =        "Evolving a storage block endurance classifier for
                 Flash memory: A trial implementation",
  booktitle =    "11th IEEE International Conference on Cybernetic
                 Intelligent Systems (CIS 2012)",
  year =         "2012",
  month =        "22-23 " # aug,
  pages =        "12--17",
  address =      "Limerick",
  keywords =     "genetic algorithms, genetic programming, Testing",
  DOI =          "doi:10.1109/CIS.2013.6782154",
  abstract =     "Solid State Drives (SSDs) have a number of significant
                 advantages over traditional Hard Disk Drives (HDDs) but
                 are currently far more expensive and have smaller
                 capacities. These drives are based on NAND Flash memory
                 devices, which have limited working lives. The number
                 of times locations in such devices can be successfully
                 programmed before they become unreliable is termed
                 their endurance.

                 There is currently no way to estimate accurately when a
                 location within a Flash device will fail, so
                 manufacturers give extremely conservative guarantees
                 about the number of program operations their chips can
                 endure. This paper describes a trial implementation of
                 Genetic Programming (GP) used to evolve a Binary
                 Classifier that predicts whether storage blocks within
                 Flash memory devices will still be functioning
                 correctly beyond some predefined number of cycles. The
                 classifier is supplied with only the measured program
                 and erase times from a relatively early point in the
                 lifetime of a block. Using the relationships between
                 these times, the system can accurately predict whether
                 the block will continue to function satisfactorily up
                 to a required number of cycles. Experiments on test
                 sets comprised of unseen data show that our classifier
                 obtains up to an average of 95percent accuracy across
                 30 runs.",
  notes =        "Also known as \cite{6782154}",
}

Genetic Programming entries for Damien Hogan Tom Arbuckle Conor Ryan

Citations