Dynamic Priority Scheduling for Data Set Error Correction
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Solution Overview
Problem
Existing data processing systems lack an effective method to prioritize and re-prioritize data sets based on quality, leading to incomplete error resolution and inefficient processing, especially in systems with variable data characteristics.
Innovation Solution
The implementation of a data processing system that includes an input buffer and a process scheduling circuit, which associates data sets with priority indications and modifies them based on processing through a data detector and decoder circuit, using algorithms like Viterbi or low density parity check, to optimize processing cycles and error correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a fixed priority is assigned to all data sets, then scheduling is simple, but error resolution is incomplete and processing efficiency deteriorates
Solution Approach 1:
The patent implements dynamic priority adjustment where the priority of each data set changes based on its processing history and error correction needs. The scheduling circuit continuously monitors the number of processing iterations and modifies priorities accordingly, transforming the static scheduling system into a dynamic one that adapts to real-time data quality and processing status.
Solution Approach 2:
The system incorporates feedback mechanisms where the outcome of each processing iteration is fed back into the scheduling decision. The scheduling circuit uses information about successful error corrections and remaining errors to adjust future scheduling decisions, creating a closed-loop control system that improves reliability while maintaining manageable complexity.
2Reliability
If data sets are re-processed multiple times to resolve errors, then error correction improves, but processing time and system resource consumption increase
Solution Approach 1:
The patent applies partial action by allowing selective re-processing of only those data sets that require additional processing iterations, rather than re-processing all data sets uniformly. The system performs exactly the number of iterations needed for each data set based on its error correction status, avoiding unnecessary processing time and resource consumption for data sets that have already been successfully corrected.
Solution Approach 2:
The system changes the priority parameter of data sets based on their processing iteration count and error correction status. By dynamically adjusting this parameter, the system optimizes the balance between achieving sufficient error correction and minimizing total processing time, allowing critical data sets to receive additional processing while less critical ones move forward efficiently.
3Productivity
If processing bandwidth is fully utilized, then productivity is high, but quality control of individual data sets deteriorates
Solution Approach 1:
The scheduling circuit dynamically adjusts the allocation of processing bandwidth to different data sets based on their individual quality requirements and processing status. High-priority data sets that need error correction receive proportionally more processing resources, while lower-priority data sets are processed during available bandwidth, maintaining both overall productivity and individual data quality.
Data Source
AI summary
Systems, circuits, devices and/or methods related to systems and methods for data processing, and more particularly to systems and methods for quality based scheduling processing of data sets. In some cases, a priority indication associated with a data set is modified based upon one or more factors. As an example, the priority indication may be modified based upon a number of times that a given data set processed through both a data detector circuit and a data decoder circuit.