ML-Based Iterative Decoder Prediction for Convergence Latency
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Solution Overview
Problem
Existing LDPC and turbo-code decoders lack deterministic convergence, failing to meet quality-of-service (QoS) and 'Fast-Fail' mode requirements due to variable convergence times, which are not uniform or predictable, leading to inefficiencies and potential timeouts.
Innovation Solution
A system utilizing machine learning classifiers to predict decoder success and latency by analyzing syndrome weights and error patterns, allowing for optimization of decoder parameters such as LLRs, bit-flipping thresholds, and clock frequency adjustments to ensure consistent convergence within predetermined thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If iterative decoding is used for LDPC and turbo-codes, then capacity-approaching correction capability is achieved, but convergence time becomes non-deterministic and variable
Solution Approach 1:
The patent applies preliminary action by training a machine learning classifier beforehand on a dataset of codeword features and their corresponding decoding outcomes. This pre-trained classifier then quickly predicts the convergence behavior of new codewords before actual decoding begins, enabling the system to prepare appropriate responses in advance rather than waiting for decoding to complete.
Solution Approach 2:
The patent introduces a machine learning classifier as an intermediary between the input codeword and the decoding process. This classifier analyzes features of the codeword and predicts decoding outcomes, serving as a mediator that provides advance information about convergence behavior without being part of the actual decoding algorithm itself.
2Reliability
If decoder runs for many iterations to ensure convergence, then correction accuracy improves, but latency increases and QoS requirements are not met
Solution Approach 1:
The system performs preliminary classification of codewords based on their features before committing to full decoding iterations. By predicting the likely outcome in advance, the system can make informed decisions about resource allocation and timing, preventing unnecessary long wait times while ensuring accurate correction when needed.
Solution Approach 2:
The patent implements feedback by using the classifier's predictions to inform subsequent decoding decisions. The predicted outcomes provide feedback about expected convergence behavior, allowing the system to adjust its approach based on this advance information rather than blindly executing fixed iteration counts.
3Loss of time
If decoder stops after few iterations for fast processing, then latency decreases, but convergence reliability drops and decoding may fail
Solution Approach 1:
The classifier performs preliminary assessment of each codeword's likely decoding behavior before the actual decoding begins. This advance knowledge allows the system to set appropriate iteration limits and expectations without compromising reliability, as the short processing time is paired with predictive information about the outcome.
Solution Approach 2:
The system uses the classifier's predictions to self-regulate its decoding process. By knowing in advance whether a codeword is likely to converge quickly or fail, the system can autonomously make decisions about resource allocation and timing without external intervention, optimizing both speed and reliability based on the codeword's own characteristics.
4Reliability
If uniform convergence time is enforced for all codewords, then QoS requirements are met, but some codewords may be forced to run unnecessary iterations increasing power consumption
Solution Approach 1:
The patent applies local quality by treating different codewords differently based on their individual characteristics and predicted outcomes. Instead of applying a uniform iteration limit to all codewords, the system uses the classifier's predictions to tailor the decoding process to each specific codeword's needs, allocating resources locally rather than uniformly.
Solution Approach 2:
The system changes parameters dynamically based on classifier predictions. Rather than maintaining fixed decoding parameters for all cases, the patent adjusts decoding behavior according to the predicted outcome for each codeword, optimizing the balance between QoS compliance and power consumption by adapting parameters to local conditions.
Data Source
AI summary
An illustrative embodiment of this disclosure is an apparatus, including a memory, a processor in communication with the memory, and a decoder. The processor is configured to train a classifier, calculate one or more features of a codeword, predict an outcome of decoding the codeword with the decoder, and determine, using the classifier, whether the outcome satisfies a predetermined threshold. In some embodiments, based on the outcome, the processor selects a set of decoder parameters to improve decoder performance.