Optimising flash non-volatile memory using machine learning: A project overview

Tom Arbuckle, Damien Hogan, Conor Ryan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

While near ubiquitous, the physical principles of Flash memory mean that its performance degrades with use. During fabrication and operation, its ability to be repeatedly programmed/ erased (endurance) needs to be balanced with its ability to store information over months/years (retention). This project overview describes how our modelling of data we obtain experimentally from Flash chips uniquely allows us to optimise the settings of their internal configuration registers, thereby mitigating these problems.

Original languageEnglish
Title of host publication5th Balkan Conference in Informatics, BCI 2012 - Proceedings
Pages235-238
Number of pages4
DOIs
Publication statusPublished - 2012
Event5th Balkan Conference in Informatics, BCI 2012 - Novi Sad, Serbia
Duration: 16 Sep 201220 Sep 2012

Publication series

NameACM International Conference Proceeding Series

Conference

Conference5th Balkan Conference in Informatics, BCI 2012
Country/TerritorySerbia
CityNovi Sad
Period16/09/1220/09/12

Keywords

  • Endurance
  • Flash memory
  • Machine learning
  • Memory performance optimisation
  • Non-volatile memory
  • Retention

Fingerprint

Dive into the research topics of 'Optimising flash non-volatile memory using machine learning: A project overview'. Together they form a unique fingerprint.

Cite this