Genetic programming based predictions and estimations for the endurance and retention of NAND flash memory devices

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

@PhdThesis{Hogan_2013_genetics,
  author =       "Damien Hogan",
  title =        "Genetic programming based predictions and estimations
                 for the endurance and retention of NAND flash memory
                 devices",
  school =       "University of Limerick",
  year =         "2013",
  address =      "Ireland",
  month =        nov,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://hdl.handle.net/10344/4875",
  URL =          "https://ulir.ul.ie/bitstream/handle/10344/4875/Hogan_2013_genetics.pdf",
  size =         "246 pages",
  abstract =     "The central hypothesis of this thesis is that it is
                 possible to use a supervised machine learning
                 technique, Genetic Programming (GP), to make accurate
                 predictions and estimations regarding the endurance and
                 retention of multi-level cell NAND Flash Memory
                 devices. The retention of storage locations, or blocks,
                 within these devices is the length of time for which
                 they successfully retain their data, while their
                 endurance is the number of times they can be programmed
                 and erased prior to failure. Manufacturers currently
                 place conservative specifications on their devices
                 since there is no technique available to quickly
                 determine the actual endurance and retention
                 capabilities of blocks within them.

                 An extensive empirical evaluation of a number of MLC
                 NAND Flash devices is completed, identifying features
                 for use with GP, before expressions are evolved to make
                 predictions and estimations regarding the retention and
                 endurance of blocks. The empirical evaluation
                 highlights the large variations in performance between
                 blocks in different devices of the same specification,
                 and even between blocks within the same device. As well
                 as building a data set for later use with GP, the
                 durations of program and erase operations are
                 identified as features with which to make endurance
                 predictions and estimations, while a relationship
                 between block location and endurance is also
                 established.

                 GP is employed to evolve binary classification
                 expressions, referred to as retention period
                 classifiers, to predict whether blocks will correctly
                 retain their data for a specified length of time.
                 Following this, endurance classifiers are evolved to
                 predict whether blocks will successfully complete a
                 predefined number of cycles. Finally, symbolic
                 regression expressions are evolved, building on the
                 earlier experiments, to estimate the actual number of
                 cycles each block will complete prior to failure and
                 are referred to as endurance estimators.",
  notes =        "Supervisor: Conor Ryan",
}

Genetic Programming entries for Damien Hogan

Citations