SSD Error Recovery Using Machine Learning for Latency and Failure Rate
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Solution Overview
Problem
Solid-state drives (SSDs) face challenges in efficiently selecting an optimal error recovery procedure to correct read errors, leading to increased latency and temporary blocking of NAND resources due to the use of various error correction codes and recovery flows, which are not always suitable for specific failure rates and latency issues.
Innovation Solution
A machine learning model is trained to forecast the most suitable error recovery procedure based on the current state of the SSD, considering read latency and failure rate, and executes the selected procedure to recover data, with the model being re-trained based on success or failure to improve performance over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple error recovery procedures are used to improve data recovery success rate, then reliability is improved, but latency increases and NAND resource availability decreases
Solution Approach 1:
The patent implements dynamic selection of error recovery procedures based on real-time analysis of failure characteristics. The system transitions from static, predetermined recovery sequences to dynamic, adaptive selection that adjusts the recovery procedure based on the specific failure mode detected, thereby optimizing both recovery success rate and latency.
Solution Approach 2:
The system changes operational parameters by analyzing failure rates and latency metrics to select appropriate error recovery procedures. Different procedures are parameterized with specific thresholds and conditions, allowing the system to switch between procedures based on current performance parameters and failure characteristics.
2Reliability
If multiple error recovery procedures are executed to improve data recovery, then reliability is improved, but NAND resource availability worsens due to temporary blocking
Solution Approach 1:
The system dynamically determines which error recovery procedure to execute based on real-time failure analysis, avoiding unnecessary execution of multiple procedures. This dynamic approach maintains high NAND resource availability while ensuring reliable recovery when needed.
Solution Approach 2:
The error correction unit performs self-diagnosis by analyzing failure characteristics and autonomously selects the appropriate recovery procedure without requiring external intervention or sequential trial-and-error execution of multiple procedures, thereby preserving NAND resource availability.
3Device complexity
If error recovery procedures are selected based on fixed rules, then device complexity is reduced, but adaptability to different failure rates and latency issues deteriorates
Solution Approach 1:
The system uses parameter-based decision making where failure rate thresholds and latency targets are used as input parameters to select recovery procedures. This approach maintains relatively simple control logic while achieving high adaptability through parameter adjustment based on observed failure characteristics.
Data Source
AI summary
Systems and methods for selecting an optimal error recovery procedure for correcting a read error in a solid-state drive are provided. A machine learning model is trained to forecast which error recovery procedure of a plurality of error recovery procedures is most likely to achieve a predetermined goal given a state of a solid-state drive. The predetermined goal is based on at least one of a read latency and a failure rate of the solid-state drive. A current state of the solid-state drive is determined. An error recovery procedure is selected from among the plurality of error recovery procedures by inputting the current state of the solid-state drive into the trained machine learning model, thereby triggering the trained machine learning model to output the selected error recovery procedure. The selected error recovery procedure is executed to recover data from the solid-state drive.