Modeling design constraints and biasing in simulation using BDDs
ICCAD '99 Proceedings of the 1999 IEEE/ACM international conference on Computer-aided design
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
Application of Formal Word-Level Analysis to Constrained Random Simulation
CAV '08 Proceedings of the 20th international conference on Computer Aided Verification
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
Proceedings of the 50th Annual Design Automation Conference
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Constrained random verification (CRV) methodology has been identified as an efficient solution to functional verification challenges. In practical cases, it is required to implement constraints with variable ordering to put essential efforts on cared patterns. However, handling constraints with variable ordering may encounter performance degradation of pattern generation speed and distribution. To resolve these challenges, we provide a preprocessing technique to analyze solution space by adaptively splitting ranges of variables and prove the feasibility of each subspace. This analysis allows us to perform effective range-reduction to enhance pattern generation speed and ensure the desired distribution. From the experimental results, our framework outperforms a state-of-art tool with 10X speedup in average and retains better stability of performance with the increase of number of variable orders.