ECC Statistical Model Updating for Multi-TU Data Recovery
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Solution Overview
Problem
Traditional data recovery methods using RAIN parity data are limited to correcting a single failed translation unit (TU) in a stripe, leading to increased storage overhead when attempting to recover multiple failed TUs, and are not effective in manufacturing variations in memory devices.
Innovation Solution
Implementing a combination of error correction schemes that modify the statistical modeling of the ECC process using RAIN parity data, allowing for the recovery of multiple failed TUs in a stripe without additional storage overhead, by updating the statistical model with data from other segments in the stripe.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional RAIN parity data methods are used to correct failed translation units, then a single failed TU can be recovered, but storage overhead increases when attempting to recover multiple failed TUs
Solution Approach 1:
The patent changes the parameter of error correction by transitioning from traditional RAIN parity data to a statistical modeling approach using machine learning. This allows the system to predict and correct multiple failed TUs without increasing storage overhead, as the correction capability is enhanced through algorithmic improvement rather than additional redundancy data.
Solution Approach 2:
The patent replaces the mechanical redundancy system (RAIN parity data) with an intelligent system based on statistical modeling and machine learning. Instead of relying on additional parity data to correct errors, the system uses trained models to predict failed TUs and apply corrections, substituting computational intelligence for mechanical redundancy.
2Reliability
If additional parity data is added to recover multiple failed TUs, then data reliability improves, but storage efficiency deteriorates
Solution Approach 1:
The patent changes the approach from increasing storage capacity (adding parity data) to enhancing correction algorithms (statistical modeling). This allows multiple failed TUs to be recovered while maintaining the same storage efficiency, as the improvement comes from intelligent prediction rather than additional redundancy.
Solution Approach 2:
The patent substitutes mechanical redundancy (additional parity data) with intelligent error correction (machine learning models). This replacement enables the system to achieve higher reliability without sacrificing storage efficiency, as the intelligent system can correct multiple errors using the same amount of stored data.
3Ease of manufacture
If traditional error correction is used, then implementation is simple, but tolerance to manufacturing variations is poor
Solution Approach 1:
The patent replaces simple traditional error correction with advanced statistical modeling and machine learning. Although this increases implementation complexity, it significantly improves tolerance to manufacturing variations by using trained models that can adapt to and correct errors arising from manufacturing imperfections that traditional methods cannot handle.
Data Source
AI summary
Exemplary methods, apparatuses, and systems include receiving a request for a segment of data. The requested segment data is one of a plurality of segments of data in a stripe of data. A failure to decode the requested segment is detected. Each of the plurality of segments in the stripe other than the requested segment are read. Reading each segment includes reading raw encoded data and attempting to decode the raw encoded data, the result of reading each segment including decoded data when decoding is successful and the raw encoded data when decoding fails. A combined result of each read is generated. The combining includes combining decoded data for segments that were successfully decoded and the raw encoded data for segments for which decoding failed. A statistical model for the requested segment is updated using the combined result. The requested segment is decoded using the updated statistical model.