Memory Data Partitioning for Content-Aware Error Correction
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Solution Overview
Problem
Existing memory devices face data corruption issues due to unintended errors during writing, reading, storing, or processing, which can lead to data loss and system crashes, especially near the end of NAND's lifespan, and current error correction methods assume uniform data distribution without leveraging unique source statistics.
Innovation Solution
A memory controller that segments encoded data into multiple partitions with unique source statistics, applying optimized decoder parameters to each segment, and uses transformations like the Burrows-Wheeler transform to rearrange data, allowing for improved error correction capabilities and reduced decoding latency and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is treated as homogeneous output from a single source with default ECC parameters, then device complexity is reduced, but correction capability and reliability deteriorate
Solution Approach 1:
The patent segments encoded data into multiple partitions based on source statistics characteristics. Each partition is decoded using optimized parameters specific to its source type, rather than applying a single uniform decoding approach to all data. This segmentation enables targeted error correction for different data sources while maintaining manageable decoder complexity through modular processing.
Solution Approach 2:
The patent applies local quality by using different decoder parameters for different segments of data based on their source statistics. Each partition receives customized decoding parameters optimized for its specific characteristics, improving correction capability for each local segment while the overall system remains structured and manageable.
2Reliability
If optimized decoder parameters are applied to each segment, then correction capability is improved, but decoding time and power consumption increase
Solution Approach 1:
The patent performs preliminary action by pre-calculating and storing optimized decoder parameters for different source statistics types. During decoding operations, the system quickly identifies the source type and retrieves the corresponding pre-optimized parameters, avoiding the need for real-time parameter optimization and reducing decoding latency while maintaining high correction capability.
3Reliability
If data is segmented into multiple partitions with unique source statistics, then correction capability is enhanced, but device complexity increases
Solution Approach 1:
The patent divides encoded data into multiple partitions based on source statistics, allowing each partition to be processed with specialized decoding parameters. This segmentation enhances correction capability by tailoring the decoding approach to each data source's characteristics while maintaining a structured, modular decoder architecture.
Solution Approach 2:
The patent implements a universal decoder framework that can handle multiple data source types through a single multi-functional decoding structure. The decoder is designed to accommodate different source statistics and parameter sets within a unified architecture, reducing the need for entirely separate decoding systems for each data type.
Data Source
AI summary
Disclosed herein are memory devices, systems, and methods of encoding and decoding data. In one aspect, an encoded data chunk is received and segmented into data segments with similar features. Each segment can be decoded based on its features. Data can also be rearranged and partitioned so as to minimize an entropy score that is based on the size and entropy of the data partitions. The approach is capable of enhancing performance, reducing decoding latency, and reducing power consumption.