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Flash memory is prone to failures as the number of program-erase cycles increases, resulting in an increase in the bit error rate. Once the bit error count exceeds a certain threshold, error correction engines are either incapable of continuing to correct the errors efficiently or they may fail entirely.

This leads to an interest in learning the behavior of the error count increase and obtaining an ability to make failure predictions.

This talk will tackle this problem using a machine learning approach, although standard ML techniques may not work well with the particular data in hand.

The talk will also cover various classification methods that address such class imbalance, including cost-sensitive boosting techniques, bagging procedures, ensemble support vector machines and cost-sensitive neural networks.

Flash Memory Summit
Mar 15, 2021
Dec 16, 2020

This resource is no longer available.