Writing testbenches: functional verification of HDL models
Writing testbenches: functional verification of HDL models
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Coverage directed test generation for functional verification using bayesian networks
Proceedings of the 40th annual Design Automation Conference
CODES+ISSS '05 Proceedings of the 3rd IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis
A PD-based methodology to enhance efficiency in testbenches with random stimulation
Proceedings of the 22nd Annual Symposium on Integrated Circuits and System Design: Chip on the Dunes
A cognitive tutoring agent with episodic and causal learning capabilities
AIED'11 Proceedings of the 15th international conference on Artificial intelligence in education
Functional Verification of DMA Controllers
Journal of Electronic Testing: Theory and Applications
Journal of Electronic Testing: Theory and Applications
Coverage-Directed Test Generation Automated by Machine Learning -- A Review
ACM Transactions on Design Automation of Electronic Systems (TODAES)
A computational model for causal learning in cognitive agents
Knowledge-Based Systems
Manipulation of Training Sets for Improving Data Mining Coverage-Driven Verification
Journal of Electronic Testing: Theory and Applications
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Today directed random simulation is one of the most commonly used verification techniques. Because this technique in no proof of correctness, it is important to test the design as complete as possible. But this is a hard to reach goal, that needs a lot of computing power and much human interaction. There has been a proposal for using Bayesian networks to implement an automatic feedback loop (Shai Fine et al, 40th Design Automation Conference, 2003). In addition, this paper introduces another implementation of an automatic feedback loop using data mining techniques. Both approaches are applied to the same design and the results are compared.