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.

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

ZAMBELLI, Cristian;RIGUZZI, Fabrizio;LAMMA, Evelina;OLIVO, Piero;
2017

Abstract

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.
978-1-5090-3274-7
3D NAND Flash; Machine Learning
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/2373784
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