Abstract
Constrained random simulation is becoming the mainstream methodology to verify system-wide properties in functional verification. It is a must to develop a high-throughput constrained random pattern generator, which is able to support arbitrary distribution. In this paper, we propose a novel range-splitting heuristic and a solution-density estimation technique to conquer the challenges of random pattern generators proposed in the recent literature. The solution densities can significantly increase by pruning infeasible subspaces. On the other hand, the estimated solution densities stored on a range-splitting tree statistically predict the distribution of solutions. Therefore, the generated patterns are ensured to meet the desired distribution with high throughput. Experimental results show that our framework achieves more than 10X speedup on average when compared to a commercial generator.
Published Version
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