Content-Aware SSD Decoding Using Data Pattern Parameters
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Solution Overview
Problem
Conventional soft Error Code Correction (ECC) decoders for 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 implementation of content-aware decoding methods that estimate data values using source and channel log-likelihood ratios, determine data pattern parameters, and perform decoding processes to improve correction capability, reduce latency, and optimize power consumption by leveraging underlying data statistics and patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional soft ECC decoders use default parameters assuming uniform distribution of data, then the decoder can operate without prior knowledge of data statistics, but the decoding accuracy deteriorates due to wrong estimation of underlying statistics
Solution Approach 1:
The system performs preliminary actions by collecting data samples during the write operation and estimating underlying statistics before the actual decoding process. This preliminary statistical estimation is stored and reused during subsequent read operations, eliminating the need to assume uniform distribution while avoiding the cost of real-time statistics collection during decoding.
Solution Approach 2:
The system prepares correction capabilities in advance by estimating data statistics during the write phase and storing these estimates. When reading data, these pre-computed statistical estimates cushion against the uncertainties of unknown data distributions, preventing decoding failures before they occur.
2Measurement precision
If the decoder adjusts parameters based on estimated statistics, then decoding accuracy may improve, but the number of bit flips increases leading to wrong estimation and potential decoding failure
Solution Approach 1:
The system applies partial action by using a limited number of data samples collected during write operations to estimate statistics, rather than processing the entire data set. This partial estimation is sufficient to improve decoding accuracy without causing excessive bit flips that would lead to wrong statistics estimation.
3Device complexity
If conventional decoders use inaccurate decoding parameters, then the device complexity remains low, but the power consumption increases and decoding latency increases
Solution Approach 1:
The system performs statistics estimation and parameter optimization in advance during the write operation, storing the results for later use. This preliminary action eliminates the need for complex real-time parameter adjustment mechanisms, maintaining low device complexity while reducing power consumption and latency during read operations.
4Reliability
If the system collects data samples during write to estimate statistics, then the correction capability improves, but the write operation time increases
Solution Approach 1:
The system uses a partial set of data samples collected during the write operation to estimate statistics, rather than processing the complete data set. This partial estimation provides sufficient correction capability while minimizing the time penalty during write operations.
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.