Memory Encoder Parity Generation Using Canonical Coefficients
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Solution Overview
Problem
As memory devices become denser, they are more prone to errors due to factors like storage charge loss, random telegraph signal effects, and cosmic rays, especially in multi-level architecture where signal levels are close, leading to increased error probability and decreased read/write margins.
Innovation Solution
The implementation of a method to compute parity data using pre-computed canonical coefficients, which allows for efficient error protection by generating parity data without iterative calculations, thereby reducing computational resources and latency, and enabling error correction in memory systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If memory devices use multi-level architecture to increase storage capacity, then storage density is improved, but error probability increases due to decreased read/write margins
Solution Approach 1:
The data is divided into multiple segments with separate parity bits calculated for each segment using different canonical coefficients. This segmentation allows independent error detection and correction for each segment, improving overall reliability while maintaining high storage density through multi-level architecture.
Solution Approach 2:
Canonical coefficients are pre-computed and stored in a lookup table before actual data encoding. This preliminary action eliminates iterative calculations during runtime, reducing latency and computational complexity while enabling fast error protection for high-capacity memory operations.
2Reliability
If iterative calculations are used to generate parity data, then error protection is achieved, but computational resources and latency increase
Solution Approach 1:
Canonical coefficients are pre-computed and stored in a lookup table before actual data encoding. This preliminary action eliminates iterative calculations during runtime, reducing latency and computational complexity while enabling fast error protection.
Solution Approach 2:
Instead of performing iterative calculations to generate parity data, the system uses pre-computed canonical coefficients copied from a lookup table. This copying approach replaces complex computational processes with simple data retrieval, significantly reducing latency while maintaining error protection capabilities.
3Reliability
If iterative parity generation is implemented, then error correction capability is provided, but logic complexity increases
Solution Approach 1:
Canonical coefficients are pre-computed and stored in a lookup table, eliminating the need for complex iterative calculation logic during runtime. This preliminary preparation simplifies the encoder logic to basic table lookups and XOR operations, reducing device complexity while maintaining full error correction capability.
Solution Approach 2:
The system replaces complex iterative parity generation logic with simple copying operations from pre-computed lookup tables. This substitution dramatically reduces logic complexity by replacing multi-step computational algorithms with single-step data retrieval and combination operations.
Data Source
AI summary
The present disclosure relates to apparatuses and method for encoding using error protection codes. An example apparatus comprises circuitry, for instance, including an encoder configured to compute parity data based, at least in part, on program data and on predetermined coefficient data. The predetermined coefficient data is determined independent of the program data.