Dependent State Element Identification in Model Verification
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Solution Overview
Problem
The computational cost of verifying complex system designs increases exponentially with the number of state elements, making it inefficient to identify dependent state variables and streamline the verification process.
Innovation Solution
The system partitions the model into smaller, independent reachable state overapproximations, truncates reachability analysis, and prunes non-contributing variables to reduce complexity and computational resources, using binary decision diagrams to identify dependent state variables and optimize reachability calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the number of state variables in the model is reduced to lower computational cost, then verification efficiency is improved, but the accuracy of the system behavior description may be compromised
Solution Approach 1:
The patent extracts and eliminates dependent state variables from the model. By identifying state variables that can be expressed as combinational functions of other state variables, the system removes redundant variables while preserving the essential behavior description, thus reducing computational cost without sacrificing verification accuracy
Solution Approach 2:
The patent changes the representation of state variables by expressing dependent variables as functions of independent variables. This parameter transformation allows the model to use fewer state variables while maintaining equivalent descriptive power, resolving the contradiction between model size and accuracy
2Use of energy by stationary object
If the model complexity is reduced by eliminating dependent variables, then computational resource consumption is decreased, but the identification process itself becomes more complex
Solution Approach 1:
The patent performs preliminary analysis to identify dependent state variables before the main verification process. By pre-computing the dependency relationships and eliminating redundant variables in advance, the system reduces the complexity of subsequent verification operations and overall computational resource consumption
Solution Approach 2:
The patent introduces an intermediary dependency analysis phase that mediates between the full model and the reduced model. This intermediate step systematically identifies and classifies dependent variables, making the complexity management tractable and enabling efficient verification with reduced state variables
Data Source
AI summary
Methods, systems and software products are provided to enhance the scalability of dependent state analysis element identification. In a method of partitioning a model representing a state machine, a variable is selected from the variables of the model, and a first set of variables are identified that support the selected variable. Then a second set of variables is identified that have overlapping support of the first set of variables. The second set of variables is a partition suitable for use in determining an overapproximation of the reachable states of the selected variable.