Characterization of TLC 3D-NAND Flash Endurance through Machine Learning for LDPC Code Rate Optimization

The advent of the 3D-NAND Flash memories introduced significant issues in terms of characterization and system-level optimization that can be performed to increase the memory reliability over its lifetime. Indeed, the knobs that a system designer can leverage to this extent are many. In this work we show that the application of machine learning algorithms like data clustering on a large characterization data set of TLC 3D-NAND Flash devices can help the designers in optimizing the countermeasures for improving the memory reliability while reducing their implementation cost.

Titolo: Characterization of TLC 3D-NAND Flash Endurance through Machine Learning for LDPC Code Rate Optimization
Autori: 
ZAMBELLI, Cristian
RIGUZZI, Fabrizio
LAMMA, Evelina
OLIVO, Piero
mostra contributor esterni 
Data di pubblicazione: 2017
Handle: http://hdl.handle.net/11392/2373784
ISBN: 978-1-5090-3274-7
Appare nelle tipologie:04.2 Contributi in atti di convegno (in Volume)

File in questo prodotto:
Non ci sono file associati a questo prodotto.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
 
 
 
Powered by IRIS-about IRIS-Utilizzo dei cookie
Logo CINECA  Copyright © 2022