Self-configuring Decoder for Non-volatile Memory
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Solution Overview
Problem
Current tiered decoding designs for non-volatile memory, such as BFA decoders, require laborious manual tuning of parameters like the bit flip threshold, which is inefficient and can lead to sub-optimal decoding performance, especially when switching between different LDPC matrices, resulting in increased latency and error rates.
Innovation Solution
A self-configuring decoder that uses feedback control systems to dynamically adjust tuning parameters, such as the bit flip threshold and syndrome-weight mappings, based on real-time performance characteristics, allowing for improved decoding efficiency and accuracy by reducing latency and mis-corrects through a PID controller.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual tuning of decoder parameters is used, then decoding performance can be optimized for specific conditions, but the process becomes laborious and time-consuming, especially when switching between different LDPC matrices
Solution Approach 1:
The decoder automatically selects and configures tuning parameters based on the input codeword characteristics and error conditions, eliminating the need for manual intervention. The system self-adjusts the bit flip threshold and syndrome-weight mappings by analyzing the decoded output and performance metrics, then autonomously optimizes parameters for subsequent decoding operations.
Solution Approach 2:
The system implements a feedback mechanism where the decoder monitors its own performance characteristics (such as bit-error rate and decoding success) and uses this information to dynamically adjust tuning parameters. The feedback loop compares expected vs. actual decoding performance and modifies parameters accordingly to maintain optimal operation across varying conditions.
2Device complexity
If fixed tuning parameters are used in the decoder, then the device complexity is reduced, but the adaptability to varying error conditions and different LDPC matrices deteriorates
Solution Approach 1:
The decoder transitions from static fixed parameters to dynamic adjustable parameters. The tuning parameters (bit flip threshold, syndrome-weight mappings) are no longer fixed but can be modified in real-time based on the decoding conditions, error patterns, and performance metrics. This dynamic adaptation allows the same hardware structure to handle multiple LDPC matrices and error conditions effectively.
Solution Approach 2:
The system changes operational parameters (tuning parameters) based on the specific decoding task and error conditions. By adjusting parameters such as the bit flip threshold and syndrome-weight mappings according to the input characteristics and observed performance, the decoder maintains high adaptability without requiring complex structural modifications. The parameter space is expanded to include multiple configurable values for each tuning parameter.
3Reliability
If laborious manual tuning is performed to optimize decoding performance, then bit-error rate performance improves, but the overall productivity and decoding throughput decrease
Solution Approach 1:
The system performs preliminary configuration of tuning parameters automatically based on the input codeword characteristics before the actual decoding process begins. By pre-selecting appropriate parameter settings based on error patterns, codeword properties, and historical performance data, the decoder is ready for immediate high-speed operation without requiring time-consuming manual tuning for each decoding task.
Data Source
AI summary
Self-configuring error control coding in a memory device provides flexibility in terms of decoding latency, mis-correct probability, and bit-error rate performance, and avoids labor-intensive trial-and-error configuration of decoding algorithm tuning parameters, such as bit flip algorithm thresholds and syndrome-weight maps. A self-configuring decoder for error control coding allows dynamic trading of error floor performance for error rate performance and vice versa based on the performance characteristics of the decoding process.