Automated Constraint Conflict Analysis in Electronic Design Verification
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Solution Overview
Problem
Current methods for analyzing formal constraint conflicts in electronic design verification are manual, labor-intensive, and lack detailed analysis, making it difficult to locate the root cause of conflicts in complex systems of constraints.
Innovation Solution
A computer-implemented method that identifies mutually conflicting assumptions, groups them, iteratively disables conflicting assumptions, generates trace pairs to depict scenario differences, and compares signals between traces to identify and display conflict propagation paths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to analyze conflicting constraints, then detailed analysis can be performed, but the process is very labor intensive and time-consuming
Solution Approach 1:
The patent replaces manual mechanical analysis with an automated computer-based system that uses formal verification techniques. The system automatically generates test cases, executes them against the constrained system, and identifies conflicts between constraints without human intervention, thereby eliminating the labor-intensive manual process while maintaining detailed analysis capability.
Solution Approach 2:
The system enables self-service by allowing the computer to automatically perform the entire conflict analysis process. The automated system generates test cases, executes them, analyzes results, and identifies constraint conflicts independently, freeing analysts from manual work while providing comprehensive conflict detection and detailed analysis.
2Reliability
If the complexity of the system of constraints increases, then more comprehensive coverage is achieved, but it becomes difficult or impossible to precisely locate the root cause of conflicts
Solution Approach 1:
The patent segments the complex constraint analysis by generating individual test cases that each target specific constraint interactions. The system breaks down the overall conflict detection into discrete test executions, where each test case focuses on a particular set of constraints, making it easier to identify which specific constraints are in conflict without being overwhelmed by the overall system complexity.
Solution Approach 2:
The patent introduces test cases as intermediary objects that mediate between the complex constraint system and the analysis process. These test cases serve as controlled inputs that trigger specific constraint violations, allowing the system to indirectly probe the constraint system and identify conflicts through the test results rather than directly analyzing the complex constraint interactions.
3Productivity
If automated analysis is implemented, then productivity increases, but current automated solutions only identify conflicting assumptions without providing detailed analysis
Solution Approach 1:
The patent performs preliminary action by generating and executing test cases before final conflict identification. The system first creates targeted test cases that are designed to expose specific constraint conflicts, executes them to gather evidence, and then uses this pre-collected information to provide detailed conflict analysis. This preliminary test execution phase enables both automation and detailed insight.
Solution Approach 2:
The patent implements feedback by using test execution results to inform the conflict analysis process. The system executes test cases against the constrained system, collects information about which constraints are violated and under what conditions, and then uses this feedback to identify and report specific constraint conflicts with detailed information about their causes and interactions.
Data Source
AI summary
The present disclosure relates to a method for electronic design verification. Embodiments may include receiving, using at least one processor, an electronic design and identifying one or more assumptions associated with the electronic design that are mutually in conflict. Embodiments may further include grouping the one or more assumptions that are mutually in conflict into a conflicting group of assumptions and iteratively disabling at least one of the conflicting group of assumptions. Embodiments may include generating at least one trace pair depicting a scenario where an assumption from a disabled set holds in a first trace but is violated in a second trace. Embodiments may further include identifying at least one signal associated with the first trace and at least one signal associated with the second trace and comparing the at least one signal associated with the first trace and the at least one signal associated with the second trace.