A robust general constrained random pattern generator for constraints with variable ordering

Abstract

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.

A robust constraint solving framework for multiple constraint sets in constrained random verification

“…The previous studies [5][6][7][8][9][10][11][12][13][14][15][16] are restricted to a single set of constraints and may cause either distribution violation or serious speed degradation if treating various sets of constraints independently.…”

“…In this paper, we adopt the aforementioned idea of range-splitting trees [15][16] constraints to infer the infeasible subspaces under another set of constraints. Specifically, a range-splitting tree is constructed incrementally from existing trees.…”