Data analysis & prediction for NAND flash decoding status

Yen Chin Liao, Ching Hui Huang, Cloud Zeng, Hsie-Chia Chang

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

3 Scopus citations

Abstract

This paper investigates the feasibility of predicting the NAND flash decoding status by machine learning algorithms. The memory system can handle the future decoding failure in advance according to the prediction results so that to relieve the penalties. Several data preprocessing techniques to improve the accuracy are addressed. A thorough analysis flow is given and the experimental results show significant improvements. Incorporating with proper memory error handling schemes, a 34% improvement in throughput can be achieved.

Original languageEnglish
Title of host publication2017 IEEE 9th International Memory Workshop, IMW 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509032723
DOIs
StatePublished - 5 Jun 2017
Event9th IEEE International Memory Workshop, IMW 2017 - Monterey, United States
Duration: 14 May 201717 May 2017

Publication series

Name2017 IEEE 9th International Memory Workshop, IMW 2017

Conference

Conference9th IEEE International Memory Workshop, IMW 2017
CountryUnited States
CityMonterey
Period14/05/1717/05/17

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