Unified Verification Coverage Model for Multi-Engine Data Merging
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Solution Overview
Problem
Current verification systems face challenges in combining coverage grades from different verification engines, leading to confusion and inefficiency, especially when static coverage data from formal verification is combined with dynamic coverage data from simulation or emulation, as each engine uses unique methods and metrics.
Innovation Solution
A method and system for merging verification data from multiple engines into a combined coverage model, using a processor to calculate a unified coverage grade for each entity based on predetermined or user-defined rules, allowing for efficient roll-up calculations and presentation of results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If coverage data from multiple verification engines are combined, then the completeness and reliability of verification coverage is improved, but the complexity of the verification system increases
Solution Approach 1:
The verification system is segmented into multiple independent verification engines (formal verification, simulation, emulation) that each generate coverage data separately. The coverage model is also segmented into hierarchical levels (system level, module level, component level) that can be processed and combined systematically. This segmentation allows complex verification tasks to be divided into manageable parts while maintaining overall reliability.
Solution Approach 2:
The patent merges coverage data from multiple verification engines by creating a unified coverage model that integrates coverage grades from formal verification, simulation, and emulation engines. The merging process combines coverage entities and calculates aggregate coverage metrics, consolidating data from diverse sources into a single coherent verification status that improves reliability without requiring manual integration.
2Measurement precision
If multiple verification engines with different methods are used, then the thoroughness of verification is improved, but the difficulty of merging and interpreting results increases
Solution Approach 1:
The coverage model is designed with universal structures that can accommodate multiple verification methods. Coverage entities are defined in a unified manner that works across formal verification, simulation, and emulation engines. The model uses standardized coverage metrics and hierarchical organization that can represent verification results from different engines uniformly, making it easier to merge and interpret results from diverse verification approaches.
Solution Approach 2:
The unified coverage model acts as an intermediary layer between multiple verification engines and the final verification assessment. It receives coverage data from different engines, standardizes the information, and provides a consolidated view. This intermediary structure translates diverse verification results into a common framework, reducing the difficulty of merging and interpreting results from multiple engines with different methods.
3Reliability
If coverage grades from multiple engines are aggregated, then a comprehensive verification status is achieved, but the time required for processing increases
Solution Approach 1:
The coverage model is prepared in advance with predefined hierarchical structures and coverage entity definitions. Verification engines can populate this pre-configured model as they execute, rather than requiring post-processing aggregation. Coverage data is collected and organized during the verification process itself, reducing the time required for final aggregation and analysis while maintaining comprehensive verification status accuracy.
Solution Approach 2:
The coverage model continuously accumulates verification data from multiple engines as they execute, rather than requiring batch processing after all verification completes. The system maintains running coverage grades that are updated in real-time as verification progresses, allowing comprehensive verification status to be achieved progressively without a final time-consuming aggregation step.
Data Source
AI summary
A method for combining verification data may include using a processor, obtaining verification data and a verification model from each of a plurality of verification engines relating to different verification methods, the verification data relating to a plurality of verification tests that were conducted on a design under test (DUT) using the plurality of verification engines; using a processor, merging the verification models obtained from the plurality of verification engines into a merged verification model; using a processor, calculating a combined verification metric grade for a plurality of verification entities in the merged verification model using verification metric grades for each of the plurality of verification entities calculated from the verification data obtained from the plurality of engines and applying a combined verification metric grade rule; and outputting the combined verification metric grade via an output device.