An optimal machine learning classification model for flash memory bit error prediction

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

2 Citations (Scopus)

Abstract

NAND flash memory is now almost ubiquitous in the world of data storage. However, NAND wears out as it is used, and manufacturers specify the number of times a device can be rewritten (known as program-erase cycles) very conservatively to account for quality variations within and across devices. This research uses machine learning to predict the true cycling level each part of a NAND device can tolerate, based on measurements taken from the device as it is used. Custom-designed hardware is used to gather millions of data samples and eight machine learning classification methods are compared. The classifier is then optimised using ensemble and knowledge-based techniques. Two new subsampling methods based on the error probability density function are also proposed.

Original languageEnglish
Title of host publicationStudies in Computational Intelligence
PublisherSpringer-Verlag GmbH and Co. KG
Pages89-110
Number of pages22
DOIs
Publication statusPublished - 2019

Publication series

NameStudies in Computational Intelligence
Volume801
ISSN (Print)1860-949X

Keywords

  • Classification
  • Classifier ensemble
  • Error rate prediction
  • Flash memory
  • Machine learning
  • Subsampling

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