mlsanityData to 14 Oct 2026

Detecting Stimuli with Novel Temporal Patterns to Accelerate Functional Coverage Closure

arXiv 2407.02510 ↗First posted 19 Jun 2024cs.SE cs.LGcited by 2

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Abstract (arXiv)

Novel test selectors have demonstrated their effectiveness in accelerating the closure of functional coverage for various industrial digital designs in simulation-based verification. The primary advantages of these test selectors include performance that is not impacted by coverage holes, straightforward implementation, and relatively low computational expense. However, the detection of stimuli with novel temporal patterns remains largely unexplored. This paper introduces two novel test selectors designed to identify such stimuli. The experiments reveal that both test selectors can accelerate the functional coverage for a commercial bus bridge, compared to random test selection. Specifically, one selector achieves a 26.9% reduction in the number of simulated tests required to reach 98.5% coverage, outperforming the savings achieved by two previously published test selectors by factors of 13 and 2.68, respectively.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 3 of its 15 references that are in the map. First posted on arXiv 19 Jun 2024.

Builds on

3 of 15 references.

References · 3

  1. 2017Attention Is All You Needcited by 60,361
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  3. 2022Using Neural Networks for Novelty-based Test Selection to Accelerate Functional Coverage Closurecited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.