Late Breaking Results: Test Selection For RTL Coverage By Unsupervised Learning From Fast Functional Simulation

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Functional coverage closure is an important but RTL simulation intensive aspect of constrained random verification.   To reduce these computational demands, we propose test selection for functional coverage via machine learning (ML) based anomaly detection in the structural coverage space of fast functional simulators. We achieve promising results on two units from a state-of-the-art production GPU design. With our approach, an up to 85\% RTL simulation runtime reduction can be achieved when compared to baseline constrained random test selection while achieving the same RTL functional coverage.


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