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.
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Titolo: | Characterization of TLC 3D-NAND Flash Endurance through Machine Learning for LDPC Code Rate Optimization | |
Autori: | ||
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) |