Syndrome-Guided Neural Decoder Selection for Error Correction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Electronic devices face challenges in ensuring reliable data integrity due to data errors and corruption during operations, which existing error correction techniques struggle to address effectively.
Innovation Solution
An electronic device utilizing an artificial neural network for error correction decoding, where a decoding controller inputs a primary syndrome vector and a parity check matrix to select between different error correction decoding algorithms based on the neural network's output, enhancing the efficiency of error correction processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple error correction decoding algorithms are used to ensure data reliability, then error correction capability is improved, but device complexity and processing time increase
Solution Approach 1:
An artificial neural network is introduced as an intermediary component between the syndrome vector input and the error correction decoding algorithms. The neural network processes the syndrome vector and outputs probability predictions for different decoding algorithms, enabling intelligent selection without requiring manual configuration or complex decision logic. This intermediary layer simplifies the overall system architecture while maintaining high error correction capability through algorithm selection based on predicted success probabilities.
2Reliability
If multiple error correction decoding algorithms are executed to improve data reliability, then error correction capability is improved, but processing speed and throughput decrease
Solution Approach 1:
The artificial neural network performs preliminary analysis of the syndrome vector before executing error correction decoding algorithms. By predicting the success probability of different algorithms in advance, the system can select the most promising algorithm(s) beforehand, avoiding unnecessary execution of algorithms with low success probabilities. This preliminary action significantly reduces processing time and increases throughput while maintaining high error correction capability through informed algorithm selection.
3Reliability
If multiple error correction decoding algorithms are used to enhance data reliability, then error correction capability is improved, but power consumption increases
Solution Approach 1:
Instead of executing multiple error correction decoding algorithms exhaustively, the system uses the artificial neural network to predict and select only the necessary algorithm(s) with the highest success probabilities. This partial action approach achieves effective error correction by focusing computational resources on the most promising algorithms, thereby reducing overall power consumption while maintaining or improving error correction capability compared to trying all algorithms.
Data Source
AI summary
The present technology includes an electronic device and a method of operating the same using an artificial neural network. The electronic device according to the present technology includes a decoding controller inputting a primary syndrome vector generated based on a read vector and a parity check matrix to a trained artificial neural, and selecting any one of a first error correction decoding algorithm and a second error correction decoding algorithm based on an output of the trained artificial neural network corresponding to the input, and an error correction decoder performing error correction decoding on a read vector using the selected error correction decoding algorithm. The output of the trained artificial neural network includes a first predicted value indicating a probability that a first error correction decoding using the first error correction decoding algorithm is successful.