Stimulus generation for constrained random simulation
Proceedings of the 2007 IEEE/ACM international conference on Computer-aided design
Random stimulus generation using entropy and XOR constraints
Proceedings of the conference on Design, automation and test in Europe
Proceedings of the 2009 Asia and South Pacific Design Automation Conference
A Markov Chain Monte Carlo Sampler for Mixed Boolean/Integer Constraints
CAV '09 Proceedings of the 21st International Conference on Computer Aided Verification
Random stimulus generation with self-tuning
CSCWD '09 Proceedings of the 2009 13th International Conference on Computer Supported Cooperative Work in Design
Towards efficient sampling: exploiting random walk strategies
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Proceedings of the International Conference on Computer-Aided Design
Simplifying Boolean constraint solving for random simulation-vector generation
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
A robust general constrained random pattern generator for constraints with variable ordering
Proceedings of the International Conference on Computer-Aided Design
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To verify system-wide properties on SoC designs in Constrained Random Verification (CRV), the default set of constraints to generate patterns could be overridden frequently through the complex testbench. It usually results in the degradation of pattern generation speed because of low hit-rate problems. In this paper, we propose a technique to preprocess the solution space under each constraint set. Regarding the similarity between constraint sets, the infeasible subspaces under a constraint set help identify the infeasible subspaces under another constraint set. The profiled results under each constraint set are then stored in a distinct range-splitting tree (RS-Tree). These trees accelerate pattern generation under multiple constraint sets and, simultaneously, ensure the produced patterns are evenly-distributed. In our experiments, our framework achieved 10X faster pattern generation speed than a state-of-art tool in average.