Ising Device Data Processing Apparatus Optimization
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Solution Overview
Problem
Existing methods for solving discrete optimization problems with inequality constraints using Ising devices face challenges due to high computational overhead, particularly when dealing with a large number of coefficients related to constraint terms, leading to increased calculation time and data transfer requirements.
Innovation Solution
A data processing apparatus and method that reduces computational overhead by storing and updating only the necessary weights related to auxiliary variables, allowing for efficient calculation of energy changes without reading all coefficients, thereby minimizing the number of addition operations and data transfer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all coefficients related to constraint terms are read and processed, then the accuracy of energy change calculation is improved, but the calculation time and data transfer overhead increase
Solution Approach 1:
The patent extracts only the necessary coefficients related to the changed variable from the complete set of constraint term coefficients. When calculating energy change for a specific variable, only the coefficients associated with that variable are retrieved and processed, while other coefficients are excluded from the calculation. This extraction approach maintains calculation accuracy for the specific variable while significantly reducing the number of coefficients that need to be read and processed, thereby reducing calculation time and data transfer overhead.
2Measurement precision
If all coefficients related to constraint terms are read and processed, then the accuracy of energy change calculation is improved, but the data transfer requirements increase
Solution Approach 1:
The patent extracts only the necessary coefficients related to the changed variable from the complete set of constraint term coefficients. When calculating energy change for a specific variable, only the coefficients associated with that variable are retrieved and processed, while other coefficients are excluded from the calculation. This extraction approach maintains calculation accuracy for the specific variable while significantly reducing the number of coefficients that need to be read and processed, thereby reducing calculation time and data transfer overhead.
3Productivity
If the number of addition operations is reduced, then the processing speed is improved, but the calculation completeness may be compromised
Solution Approach 1:
The patent extracts only the necessary coefficients related to the changed variable from the complete set of constraint term coefficients. When calculating energy change for a specific variable, only the coefficients associated with that variable are retrieved and processed, while other coefficients are excluded from the calculation. This extraction approach maintains calculation accuracy for the specific variable while significantly reducing the number of coefficients that need to be read and processed, thereby reducing calculation time and data transfer overhead.
Solution Approach 2:
The patent applies local quality by making the calculation process variable-specific. Instead of uniformly processing all coefficients for all variables, the system tailors the calculation to each variable by including only its relevant coefficients. This localized approach ensures that each energy change calculation is complete and accurate for its specific variable while avoiding unnecessary computations for other variables, thereby maintaining calculation completeness while improving processing speed.
Data Source
AI summary
A data processing apparatus configured to search for a combination of values of a plurality of state variables that minimizes or maximizes a value of an Ising-type evaluation function, when a change in a value of a first state variable is permitted, updating the value of the first state variable, updating a first local field based on a first weight value related to the first state variable, and updating a second local field based on a second weight value related to the first state variable, when the change in a value of the first auxiliary variable is permitted, updating the value of the first auxiliary variable, and updating the first local field based on a second weight value related to the first auxiliary variable.


