LDPC Decoder Architecture for On-the-Fly Syndrome Weight Updates
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional LDPC decoders face delays and increased complexity in syndrome weight computation due to the need to shift and align syndromes with hard decision bits, leading to inefficiencies in decoding processes, especially in high bit error rate scenarios.
Innovation Solution
The proposed solution involves shifting hard decision bit data instead of syndromes, reducing the number of barrel shifters required and allowing for on-the-fly syndrome weight computation, which enables real-time syndrome and syndrome weight updates, thereby reducing latency and improving decoding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If syndrome weight is computed at the end of each decoding iteration by shifting and aligning syndromes with hard decision bits, then accurate convergence detection is achieved, but decoding latency increases and decoding efficiency decreases
Solution Approach 1:
The patent performs preliminary alignment of syndromes with hard decision bits at the beginning of each decoding iteration rather than at the end. By pre-aligning the syndromes before the decoding iteration starts, the system enables real-time syndrome weight computation during the iteration without requiring additional alignment operations afterward, thus reducing decoding latency while maintaining convergence detection accuracy.
2Measurement precision
If syndromes are shifted and aligned with hard decision bits for syndrome weight computation, then accurate syndrome weight is obtained, but device complexity increases due to additional barrel shifters
Solution Approach 1:
Instead of shifting the syndromes to align with hard decision bits, the patent inverts the approach by shifting the hard decision bits to align with the syndromes. This inversion eliminates the need for separate syndrome alignment barrel shifters, reducing device complexity while still achieving accurate syndrome weight computation through the same alignment objective.
3Measurement precision
If conventional syndrome weight computation is performed at the end of decoding iterations, then convergence detection is accurate, but decoding efficiency and throughput decrease
Solution Approach 1:
The patent enables continuous syndrome weight computation throughout the decoding iteration by performing alignment at the beginning and maintaining aligned状态 during the iteration. This continuous availability of accurate syndrome weight information allows for real-time convergence detection and potential early termination, improving decoding throughput without sacrificing detection accuracy.
Data Source
AI summary
A decoder includes syndrome storage and a first barrel shifter configured to bit-shift hard decision bit data to generate shifted data that is aligned with a set of syndromes from the syndrome storage. The decoder also includes a first syndrome update circuit coupled to the first barrel shifter and configured to process the set of syndromes based on the shifted data to generate an updated version of the set of syndromes. The decoder may also be configured to perform on-the-fly syndrome weight computation.