Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Elements of information theory
Elements of information theory
Validation with guided search of the state space
DAC '98 Proceedings of the 35th annual Design Automation Conference
Functional verification methodology for microprocessors using the Genesys test-program generator
DATE '99 Proceedings of the conference on Design, automation and test in Europe
Micro architecture coverage directed generation of test programs
Proceedings of the 36th annual ACM/IEEE Design Automation Conference
Writing testbenches: functional verification of HDL models
Writing testbenches: functional verification of HDL models
Coverage directed test generation for functional verification using bayesian networks
Proceedings of the 40th annual Design Automation Conference
A Functional Validation Technique: Biased-Random Simulation Guided by Observability-Based Coverage
ICCD '01 Proceedings of the International Conference on Computer Design: VLSI in Computers & Processors
Comprehensive Functional Verification: The Complete Industry Cycle (Systems on Silicon)
Comprehensive Functional Verification: The Complete Industry Cycle (Systems on Silicon)
Functional Verification Coverage Measurement and Analysis
Functional Verification Coverage Measurement and Analysis
Microprocessor Verification via Feedback-Adjusted Markov Models
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Automatic Boosting of Cross-Product Coverage Using Bayesian Networks
HVC '08 Proceedings of the 4th International Haifa Verification Conference on Hardware and Software: Verification and Testing
| Hi-index | 0.00 |
Reaching hard-to-reach coverage events is a difficult task that requires both time and expertise. Data-driven Coverage Directed Generation (CDG) can assist in the task when the coverage events are part of a structured coverage model, but is a-priori less useful when the target events are singular and not part of a model. We present virtual coverage models as a mean for enabling data-driven CDG to reach singular events. A virtual coverage model is a structured coverage model (e.g., cross-product coverage) defined around the target event, such that the target event is a point in the structured model. With the structured coverage model around the target event, the CDG system can exploit the structure to learn how to reach the target event from covered points in the structured model. A case study of using CDG and virtual coverage to reach a hard-to-reach event in a multi-processor system demonstrates the usefulness of the proposed method.