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An SVM-Based NAND Flash Endurance Prediction Method.

Haichun Zhang1, Jie Wang2, Zhuo Chen1

  • 1School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China.

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Summary
This summary is machine-generated.

Machine learning predicts NAND flash memory endurance, optimizing wear-leveling and preventing premature device failure. This approach extends NAND flash memory lifespan by accurately forecasting remaining program-erase cycles and bit error rates.

Keywords:
NAND flash memoryenduranceraw bit errorsupport vector machinetest platform

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Materials Science

Background:

  • NAND flash memory is crucial for modern electronics but faces reliability degradation with increasing density.
  • This degradation shortens device lifespan and leads to premature replacement, causing significant waste.

Purpose of the Study:

  • To develop a machine learning-based scheme for predicting NAND flash memory endurance.
  • To optimize wear-leveling strategies and identify failing memory blocks, thereby extending device lifetime.

Main Methods:

  • A multi-class endurance prediction scheme utilizing the Support Vector Machine (SVM) algorithm.
  • Feature analysis of endurance data to identify key predictive elements.
  • Development of a feature preprocessing module on a ZYNQ-7030 chip for real-time analysis.

Main Results:

  • The proposed SVM scheme accurately predicts remaining Program-Erase (P-E) cycle levels and raw bit error rates.
  • Targeted optimization strategies were developed based on error feature analysis.
  • The SVM decision model achieved a prediction time of 37 microseconds.

Conclusions:

  • The SVM-based endurance prediction scheme effectively enhances NAND flash memory reliability.
  • This method offers significant potential for extending the operational life of storage devices and preventing data loss.