LDPC Reliability Data Updating With Selective Check-Node Input
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Solution Overview
Problem
Existing memory systems face challenges in efficiently updating reliability data while balancing error correction, latency, throughput, and power constraints, particularly in portable electronic devices using low-density parity-check (LDPC) codes.
Innovation Solution
The method involves a reliability circuit that provides specific reliability data values for hard data values and updates reliability data based on input from less than all check nodes, using a log likelihood ratio (LLR) circuit and LDPC error correction circuit with variable and check nodes, allowing for efficient error correction in both hard and soft data modes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional LDPC error correction methods are used to update reliability data, then error correction capability is maintained, but memory usage and system complexity increase
Solution Approach 1:
The patent extracts and eliminates redundant reliability data updates by recognizing that updating from all check nodes is unnecessary. The invention updates reliability data selectively based on syndrome check results, removing the need to process all check node inputs in every iteration, thereby reducing memory requirements while maintaining error correction capability.
Solution Approach 2:
The patent applies partial action by updating reliability data only when necessary (when syndrome checks indicate errors are present) and only using inputs from relevant check nodes rather than all check nodes. This selective updating approach reduces the amount of data that needs to be stored and processed, lowering memory usage while preserving essential error correction functionality.
2Reliability
If comprehensive reliability data updating is performed using all check nodes, then error correction accuracy is improved, but processing time and power consumption increase
Solution Approach 1:
The patent implements periodic action by performing syndrome checks at intervals to determine whether reliability data updates are necessary. Rather than continuously updating reliability data from all check nodes in every iteration, the system periodically assesses whether errors are present and only then performs selective updates, reducing processing time while maintaining error correction accuracy.
Solution Approach 2:
The patent uses partial action by performing incomplete updates of reliability data, processing only the necessary portions when errors are detected. This approach avoids the overhead of processing all check node inputs in every iteration, significantly reducing processing time while maintaining sufficient error correction accuracy for practical applications.
3Reliability
If full reliability data updating from all check nodes is implemented, then error correction performance is maximized, but power consumption increases
Solution Approach 1:
The patent applies partial action by performing selective reliability data updates only when syndrome checks indicate errors are present, rather than continuously updating from all check nodes. This selective approach significantly reduces the number of computational operations required, lowering power consumption while maintaining effective error correction performance for the actual error cases that occur.
Solution Approach 2:
The patent implements self-service by using syndrome check results to automatically determine whether reliability data updates are necessary. The system monitors its own error conditions and selectively activates update operations only when needed, avoiding unnecessary power consumption from continuous updates while ensuring error correction performance is maintained when errors are actually present.
4Reliability
If iterative reliability data updates are performed in each LDPC iteration, then error correction robustness is improved, but system complexity and processing overhead increase
Solution Approach 1:
The patent extracts the essential error correction functionality by performing selective reliability data updates based on syndrome check results rather than implementing full iterative updates in every LDPC iteration. This extraction approach removes unnecessary processing overhead while preserving the core robustness needed to correct errors, simplifying the overall system complexity.
Solution Approach 2:
The patent applies partial action by performing incomplete or selective reliability data updates only when syndrome checks indicate errors are present, rather than performing full iterative updates in every LDPC iteration. This approach reduces processing overhead and system complexity while maintaining sufficient error correction robustness for practical applications.
Data Source
AI summary
The present disclosure includes apparatuses and methods related to updating reliability data. A number of methods can include receiving, at a variable node, either a first reliability data value with a first hard data value or a second reliability data value with a second hard data value, sending the first hard data value or the second hard data value to each check node coupled to the variable node according to a parity check code, and updating the reliability data based on input from less than all of the check nodes.