Toward an extremely-high-throughput and even-distribution pattern generator for the constrained random simulation techniques

Constrained random simulation is becoming the mainstream methodology in functional verification. In order to achieve the verification closure, a high-throughput and evenly-distributed constrained random pattern generator has become a must. In this paper, we propose a novel Range-Splitting heuristic and a Solution-Density Estimation technique (RSSDE) to partition sample space. The chosen cutting planes target to prune more infeasible subspaces so that the solution densities in other subspaces increase correspondingly. In addition, with statistics-based analyses, the estimated solution densities precisely predict the distribution of solutions. The intermediate statistical information is recorded in a range-splitting tree (RS-tree). By top-down random walking on the RS-tree, random pattern generation produces evenly-distributed patterns with high throughput. Experimental results show that our framework guarantees evenly-distributed stimuli and achieves more than 10× speedup in average when compared to a state-of-the-art commercial generator.