Integer Scaling Determination in Fixed-Point Conversion
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Solution Overview
Problem
The conversion of floating-point implementations to fixed-point implementations for real-time calculations, such as in control and regulation systems, is time-consuming and prone to errors due to the lack of systematic assistance from programming tools, often requiring significant manual effort and expert knowledge to ensure accurate integer scaling based on physical requirements.
Innovation Solution
A method for determining integer scaling in a linking of input and output sets using a computer device with a processing unit and memory unit, where representations of operator effects are propagated to generate possible integer scalings, allowing for automated selection and output of consistent scalings that meet physical requirements without the need for simulated or measured data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual calculations and frequent trials are used for fixed-point implementation, then flexibility in handling complex cases is maintained, but time expenditure and error susceptibility increase significantly
Solution Approach 1:
The system performs preliminary static analysis of the dynamic system model to determine value ranges for floating-point variables before code generation. This preliminary determination of normalization factors and fixed-point types eliminates the need for frequent manual trials and calculations during implementation, directly reducing time expenditure while maintaining handling capability through systematic automated analysis.
Solution Approach 2:
The programming tool automatically performs the conversion from floating-point to fixed-point implementation by analyzing the dynamic system model and generating appropriate normalization factors. This self-service capability eliminates the need for manual calculations and expert intervention, reducing implementation time while maintaining the ability to handle complex cases through automated systematic analysis.
2Extent of automation
If semiautomatic fixed-point implementation based on simulated data is used, then automation level increases, but data quality requirements and generation effort increase significantly
Solution Approach 1:
The invention extracts value ranges and normalization factors directly from the static structure of the dynamic system model itself, rather than requiring external simulated or measured data. By taking the necessary information directly from the model's mathematical representation, the system achieves high automation without the burden of data generation and quality assurance.
Solution Approach 2:
The system uses the dynamic system model as an intermediary between the problem domain and the fixed-point implementation. Instead of relying on simulated data as an intermediary, the model itself serves as the direct source for determining normalization factors and fixed-point types, eliminating data generation effort while maintaining automation through systematic model analysis.
3Device complexity
If integer scaling is based on local phenomena with preset resolutions, then implementation complexity is reduced, but physical representational accuracy deteriorates
Solution Approach 1:
The system performs multiple functions through a single systematic process: it determines value ranges, calculates normalization factors, and establishes fixed-point types all based on the dynamic system model. This universal approach ensures physical representational accuracy across the entire system while maintaining implementation simplicity through automated consistent application of the same methodology throughout.
Solution Approach 2:
The invention systematically determines parameter values (normalization factors and resolutions) based on the mathematical properties of the dynamic system model rather than using preset values. This parameter determination from first principles ensures physical representational accuracy while the automated calculation process maintains implementation simplicity by eliminating manual intervention.
4Reliability
If iterative procedures with manual review are used to eliminate application errors, then solution quality improves, but productivity and time efficiency decrease
Solution Approach 1:
The system incorporates feedback mechanisms through automated consistency checking and validation of the generated fixed-point implementation against the original dynamic system model. This automated feedback ensures solution quality and reliability by systematically verifying correctness without requiring manual review cycles, thereby maintaining high productivity and development efficiency.
Solution Approach 2:
The invention performs preliminary automated validation and consistency checking during the code generation process itself, rather than requiring subsequent manual review. This preliminary action ensures solution quality is verified early in the development process, eliminating the need for iterative manual review cycles and maintaining high productivity through systematic automated verification.
Data Source
AI summary
The invention relates to a method for determining a value of an integer scaling in a linking of input sets to output sets, wherein the linking comprises operators, each of which has operator inputs and operator outputs that are at least partially linked to one another or to the input sets or to the output sets, by using a computer device having a processing unit, a memory unit, and an output unit. Representations of set objects are used to efficiently carry out rescaling operations within the linking, with up to infinitely large resolution sets. This procedure makes it possible to calculate resource-conserving integer scalings for a target system while taking secondary conditions into account.