Detecting Stimuli with Novel Temporal Patterns to Accelerate Functional Coverage Closure
Xuan Zheng, Tim Blackmore, James Buckingham, Kerstin Eder

TL;DR
This paper presents two new test selectors that detect stimuli with novel temporal patterns, significantly accelerating functional coverage closure in digital design verification compared to existing methods.
Contribution
Introduction of two novel test selectors specifically designed to identify stimuli with novel temporal patterns, improving coverage speed in simulation-based verification.
Findings
One selector reduces tests by 26.9% to reach 98.5% coverage.
Selectors outperform previous methods by factors of 13 and 2.68.
Both selectors effectively accelerate functional coverage closure.
Abstract
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…
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Taxonomy
TopicsMachine Learning and Data Classification · Anomaly Detection Techniques and Applications
