Optimising Flash non-volatile memory using machine learning: a project overview

Authors:
Tom Arbuckle;Damien Hogan;Conor Ryan
Affiliations:
University of Limerick, Limerick, Ireland;University of Limerick, Limerick, Ireland;University of Limerick, Limerick, Ireland
Venue:
Proceedings of the Fifth Balkan Conference in Informatics
Year:
2012

Citing 4
Cited 3

A study of the effect of different types of noise on the precision of supervised learning techniques

Artificial Intelligence Review
A Field Guide to Genetic Programming

A Field Guide to Genetic Programming
An overview of non-volatile memory technology and the implication for tools and architectures

Proceedings of the Conference on Design, Automation and Test in Europe
A destructive evolutionary algorithm process

Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on Bio-inspired Learning and Intelligent Systems

How early and with how little data? using genetic programming to evolve endurance classifiers for MLC NAND flash memory

EuroGP'13 Proceedings of the 16th European conference on Genetic Programming
Estimating MLC NAND flash endurance: a genetic programming based symbolic regression application

Proceedings of the 15th annual conference on Genetic and evolutionary computation
Learning predictors for flash memory endurance: a comparative study of alternative classification methods

International Journal of Computational Intelligence Studies

Quantified Score

Hi-index	0.00

Visualization

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.