Random stimulus generation with self-tuning

Zhao, Yu; Bian, Jinian; Deng, Shujun; Kong, Zhiqiu

doi:10.1109/cscwd.2009.4968035

Cited by 11 publications

(8 citation statements)

References 8 publications

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“…Some studies enhance certain requirement (either speed or even distribution) but sacrifice the other [3][4][5][6][7][8]. The acceptance and rejection (A&R) technique proposed in [7] applies random samples to produce feasible patterns.…”

Section: Introductionmentioning

confidence: 99%

“…However the pattern generation speed would be slow when constraints cannot be easily solved. As opposed to A&R technique, formal pattern generators [3][4][5][6] such as SAT solvers are robust to solve general constraints and provide unsatifiability analyses but sacrifice both speed and distribution. Further, another approach to increase success ratio for A&R technique such as RACE [8] applies interval propagation to reduce ranges of variables before sampling.…”

Section: Introductionmentioning

confidence: 99%

“…There exist several studies focusing on both pattern generation speed and distribution evenness [2][3][4][5][6][7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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

Huang

2012

Proceedings of the International Conference on Computer-Aided Design

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Huang

2012

Proceedings of the International Conference on Computer-Aided Design

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“…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.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, the formal pattern generators [11][12][13][14] such as SAT engines can perform incremental SAT solving to share the learned clauses between various sets of constraints. By inserting an extra Boolean control variable in each clause, we can switch between constraint sets.…”

Section: Introductionmentioning

confidence: 99%

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

Wu¹,

Huang²

2013

Proceedings of the 50th Annual Design Automation Conference

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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.

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From Informal Specifications to an ABV Framework for Industrial Firmware Verification

Germiniani

Bragaglio

Pravadelli

2021

VLSI-SoC: Design Trends

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Random stimulus generation with self-tuning

Abstract: Abstract

Cited by 11 publications

References 8 publications

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

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

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

From Informal Specifications to an ABV Framework for Industrial Firmware Verification

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