Information Divergence Data Processing Scheduling
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Solution Overview
Problem
Existing data processing systems face challenges in predicting the appropriate number of iterations through data detector and decoder circuits, leading to inefficiencies and waste due to variable data characteristics.
Innovation Solution
The implementation of information divergence-based data processing systems, including a data detector circuit, central memory, and scheduling circuit, which calculate quality metrics from detected outputs to optimize the application of data detection and decoding algorithms, prioritizing processing based on these metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a variable number of iterations through data detector and decoder circuits is used based on data characteristics, then processing efficiency is improved, but it becomes difficult to predict the appropriate number of iterations leading to scheduling complexity
Solution Approach 1:
The system calculates a quality metric for each data set before processing to predict the required number of iterations in advance. This preliminary assessment allows the scheduler to allocate resources efficiently without complex real-time adjustments during processing.
Solution Approach 2:
The system uses quality metrics calculated from data characteristics to provide feedback to the scheduler, enabling dynamic adjustment of iteration counts and resource allocation based on actual data conditions rather than fixed predetermined values.
2Reliability
If more iterations are performed to ensure data quality, then reliability is improved, but processing time and resource waste increase
Solution Approach 1:
The system dynamically changes the number of iterations based on calculated quality metrics for each data set. Data sets with higher quality metrics require fewer iterations, while those with lower metrics receive more iterations, optimizing both reliability and processing time.
Solution Approach 2:
Different data sets receive different numbers of iterations based on their individual quality characteristics. This localized adaptation ensures that each data set gets the appropriate processing intensity needed for its specific quality level, avoiding uniform over-processing.
3Productivity
If fewer iterations are performed to reduce processing time, then productivity is improved, but data quality and reliability deteriorate
Solution Approach 1:
The system adjusts the iteration parameter dynamically based on quality metrics. For high-quality data sets, fewer iterations are performed to maintain productivity, while lower-quality data sets receive additional iterations to ensure reliability, achieving both goals simultaneously.
4Ease of operation
If fixed iteration counts are used for all data sets, then scheduling simplicity is maintained, but processing efficiency decreases due to mismatch between actual data needs and allocated iterations
Solution Approach 1:
The system performs a preliminary quality assessment of each data set before scheduling processing. This advance evaluation enables the scheduler to assign appropriate iteration counts without complex real-time decision-making, maintaining simplicity while improving efficiency.
Solution Approach 2:
The system applies different iteration counts to different data sets based on their individual quality characteristics. This localized approach maintains scheduling simplicity through rule-based assignment while significantly improving processing efficiency compared to uniform fixed iteration counts.
Data Source
AI summary
The present inventions are related to systems and methods for information divergence based data processing. As an example, a system is disclosed that includes a scheduling circuit operable to calculate a first quality metric using a first information divergence value calculated based at least in part on the first detected output, and to calculate a second quality metric using a second information divergence value calculated based at least in part on the second detected output. A decoder input is selected based at least in part on the first quality metric and the second quality metric.