Content-Aware SSD Decoding for High Bit Error Recovery
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Solution Overview
Problem
Conventional soft Error Code Correction (ECC) decoders in solid state drives (SSDs) face issues with high bit flips, inaccurate estimation of underlying statistics, increased decoding latency, and power consumption due to incorrect decoding parameters, leading to potential data loss and reliability concerns.
Innovation Solution
The method involves obtaining data from SSDs, estimating probabilities using source and channel log-likelihood ratios, determining data pattern parameters, and performing a decoding process to enhance correction capability, reduce latency, and improve power efficiency by leveraging underlying data structures and patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional soft ECC decoders use default parameters assuming uniform data distribution, then the decoding process is simple, but the estimation of underlying statistics becomes inaccurate leading to high bit flips and wrong predictions
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing statistics from clean read data before the actual decoding process. The system performs an initial read to gather statistical information about data patterns, then uses these pre-computed statistics to guide the subsequent error correction decoding, avoiding the need to estimate statistics during the critical decoding phase.
Solution Approach 2:
The patent implements partial action by using only a portion of the read data for statistics gathering rather than the entire dataset. The system selectively samples data to build statistical models, then applies these models to correct errors in the remaining data, reducing the computational burden while maintaining accuracy.
2Adaptability or versatility
If decoder parameters are adjusted based on wrong estimated statistics, then the decoder adapts to the data, but the correction capability decreases and more bit flips occur
Solution Approach 1:
The patent implements feedback by using the statistics gathered from initial reads to continuously refine and update the decoding parameters. The system monitors decoding performance and adjusts its statistical models based on actual error patterns observed, creating a closed-loop system that improves reliability through iterative refinement rather than relying on initial wrong estimates.
3Productivity
If the decoder uses inaccurate statistics to guide decoding, then the decoding process is faster, but the power consumption increases and decoding latency increases due to failed decoding attempts
Solution Approach 1:
The patent applies preliminary action by performing statistics gathering during idle periods or initial reads before the actual decoding operation. This pre-computation of statistical models allows the main decoding process to proceed quickly using these pre-established models, reducing both power consumption and latency during critical data recovery operations.
4Loss of time
If the number of bit flips increases beyond the decoder's correction capability, then decoding may proceed quickly, but the decoding fails and data loss occurs
Solution Approach 1:
The patent implements partial action by focusing the decoding effort on correcting only the most critical errors identified through statistical analysis, rather than attempting to correct all possible bit flips. The system uses gathered statistics to identify and correct the subset of errors that are most likely to occur, achieving reliable decoding within the capability limits without unnecessary processing of impossible corrections.
Data Source
AI summary
A method and apparatus for obtaining data from a memory, estimating a probability of data values of the obtained data based on at least one of a source log-likelihood ratio and a channel log-likelihood ratio, wherein each bit in the obtained data has an associated log-likelihood ratio, determining at least one data pattern parameter for the data and performing a decoding process using the at least one data pattern parameters to determine a decoded data set.