ECC Data Processing Block Using Syndrome and ELP Decoding
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Solution Overview
Problem
Nonvolatile memory devices often experience data errors during storage and transmission due to various reasons, necessitating the inclusion of error correction mechanisms to ensure data integrity.
Innovation Solution
A data processing block comprising a syndrome computation unit, an error location polynomial (ELP) solver, and a Chien search unit, which generates odd and even syndrome values to perform decoding operations using algorithms like Berlekamp-Massey or Euclidean algorithms, enabling effective error detection and correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If error correction code (ECC) units are added to detect and correct errors in data, then data integrity is improved, but device complexity increases
Solution Approach 1:
The ECC unit is divided into multiple independent components: syndrome computation unit, ELP solver, and Chien search unit. Each component performs a specific function in the error correction process, allowing for modular design and independent optimization of each module while maintaining overall system reliability.
Solution Approach 2:
The patent introduces intermediate computational structures including syndrome values (odd and even), error location polynomials, and root-finding mechanisms. These intermediaries transform the error correction problem into a series of manageable mathematical steps, bridging the gap between raw data and corrected output while managing complexity through structured computation.
2Reliability
If syndrome computation and error correction operations are performed using traditional methods, then error detection capability is maintained, but processing speed decreases
Solution Approach 1:
The syndrome computation unit pre-computes syndrome values (both odd and even) from the received codeword before the actual error correction process. This preliminary computation organizes the error information in a structured format, enabling faster subsequent processing by the ELP solver and Chien search unit without sacrificing detection accuracy.
Solution Approach 2:
The ELP solver dynamically adapts its computation based on the syndrome values received, adjusting the error location polynomial generation process according to the specific error pattern detected. This dynamic approach allows the system to optimize processing speed for different error scenarios while maintaining robust error detection capability.
3Manufacturing precision
If multiple syndrome values and error location polynomials are computed to improve error correction accuracy, then manufacturing precision of data is improved, but loss of time increases
Solution Approach 1:
The error correction process is designed as a continuous pipeline where syndrome computation feeds directly into ELP solver, which in turn feeds the Chien search unit. This continuous flow of computation eliminates idle time between processing stages, maintaining high data correction accuracy through multiple computational steps while minimizing overall decoding time through efficient resource utilization.
Solution Approach 2:
The system employs periodic computation cycles for generating error location polynomials and searching for roots, organizing the complex mathematical operations into structured phases. This periodic structure allows for efficient resource allocation and timing optimization, balancing the need for multiple computational passes with the constraint of decoding time.
Data Source
AI summary
A data processing block that includes a syndrome computation unit suitable for generating odd syndrome values in response to a received codeword, an ELP solver suitable for generating even syndrome values, based on the odd syndrome values in a first mode, and generating an error location polynomial, based on the odd syndrome values and the even syndrome values in a second mode, and a Chien search unit suitable for generating solutions of the error location polynomial.