Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Test program generation for functional verification of PowerPC processors in IBM
DAC '95 Proceedings of the 32nd annual ACM/IEEE 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
Model checking
Hole analysis for functional coverage data
Proceedings of the 39th annual Design Automation Conference
Theoretical Advances in Neural Computation and Learning
Theoretical Advances in Neural Computation and Learning
Infiniband
Machine Learning
Machine Learning
Coverage directed test generation for functional verification using bayesian networks
Proceedings of the 40th annual Design Automation Conference
X-Gen: a random test-case generator for systems and SoCs
HLDVT '02 Proceedings of the Seventh IEEE International High-Level Design Validation and Test Workshop
Comprehensive Functional Verification: The Complete Industry Cycle (Systems on Silicon)
Comprehensive Functional Verification: The Complete Industry Cycle (Systems on Silicon)
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
Coverage-Directed Test Generation Automated by Machine Learning -- A Review
ACM Transactions on Design Automation of Electronic Systems (TODAES)
| Hi-index | 14.98 |
The initial state of a design under verification has a major impact on the ability of stimuli generators to successfully generate the requested stimuli. For complexity reasons, most stimuli generators use sequential solutions without planning ahead. Therefore, in many cases, they fail to produce a consistent stimuli due to an inadequate selection of the initial state. We propose a new method, based on machine learning techniques, to improve generation success by learning the relationship between the initial state vector and generation success. We applied the proposed method in two different settings, with the objective of improving generation success and coverage in processor and system level generation. In both settings, the proposed method significantly reduced generation failures and enabled faster coverage.