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Machine-learned assessment and prediction of robust solid state storage system reliability physics
Reliability physics of the complex memory sub-system of modern, robust solid state storage devices (SSDs) under throughput acceleration stress is analyzed leveraging Machine Learning - towards understanding their inherently designed fault-tolerance schemes that mitigate expected memory degradation m...
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Main Authors: | , , |
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Format: | Conference Proceeding |
Language: | English |
Subjects: | |
Online Access: | Request full text |
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Summary: | Reliability physics of the complex memory sub-system of modern, robust solid state storage devices (SSDs) under throughput acceleration stress is analyzed leveraging Machine Learning - towards understanding their inherently designed fault-tolerance schemes that mitigate expected memory degradation mechanisms through reliable life as a system. With the strength of multiple designed error-management schemes effectively countering multiple memory degradation mechanisms under stress, the developed empirical data based Machine Learning framework allows inferential and predictive assessments on reliable SSD design at system-level in a quantitative and pro-active manner. Such Machine-Learned quantitative assessments on the system-level health of individual devices can be utilized towards managing qualification reliability assessments, assessing dynamic throughput stress impact on design and/or decision-making on reliability of individual and populations of solid-state storage systems. |
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ISSN: | 1938-1891 |
DOI: | 10.1109/IRPS.2018.8353565 |