Machine Learning
Predictive Performance of Weghted Relative Accuracy
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Coverage directed test generation for functional verification using bayesian networks
Proceedings of the 40th annual Design Automation Conference
Subgroup Discovery with CN2-SD
The Journal of Machine Learning Research
MicroGP—An Evolutionary Assembly Program Generator
Genetic Programming and Evolvable Machines
Learning microarchitectural behaviors to improve stimuli generation quality
Proceedings of the 48th Design Automation Conference
Online selection of effective functional test programs based on novelty detection
Proceedings of the International Conference on Computer-Aided Design
Microprocessor Verification via Feedback-Adjusted Markov Models
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Novel test detection to improve simulation efficiency: a commercial experiment
Proceedings of the International Conference on Computer-Aided Design
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This work proposes a methodology of knowledge extraction from constrained-random simulation data. Feature-based analysis is employed to extract rules describing the unique properties of novel assembly programs hitting special conditions. The knowledge learned can be reused to guide constrained-random test generation towards uncovered corners. The experiments are conducted based on the verification environment of a commercial processor design, in parallel with the on-going verification efforts. The experimental results show that by leveraging the knowledge extracted from constrained-random simulation, we can improve the test templates to activate the assertions that otherwise are difficult to activate by extensive simulation.