Characterization of TLC 3D-NAND Flash Endurance through Machine Learning for LDPC Code Rate Optimization
| Autor: | Giuseppe Cancelliere, Fabrizio Riguzzi, Rino Micheloni, Evelina Lamma, Piero Olivo, Cristian Zambelli, Alessia Marelli |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Hardware_MEMORYSTRUCTURES
business.industry Computer science 3D NAND Flash Machine Learning NAND gate 02 engineering and technology Machine learning computer.software_genre 020202 computer hardware & architecture Characterization (materials science) NO Data set Flash (photography) Reliability (semiconductor) 0202 electrical engineering electronic engineering information engineering Leverage (statistics) 020201 artificial intelligence & image processing Artificial intelligence Low-density parity-check code business Cluster analysis computer |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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