Formal-Verification-Guided Bayesian Optimization for Non-Intrusive Last-Mile Coverage Closure

Key points

• Last-mile functional coverage closure for modern chip designs remains costly because a mature UVM environment exposes many controls while direct hits on the remaining bins are sparse.1
• Under a matched 20-simulation budget, Bayesian optimization (BO) is compared with continued constrained-random verification (CRV) and random search over the same AST/COI-reduced space.1
• Cleanup and grouping never add numerator bins; only simulator coverage artifacts do.1
• On OpenTitan AES, first-pass reduced-space BO reaches 241/271 bins; formal cleanup removes two denominator bins; the integrated post-cleanup stage - difficulty grouping, policy routing, and group-specific BO - adds 20 simulator-confirmed hits, yielding 261/271 raw coverage (96.31%) and 261/269 effective coverage (97.03%).1

Sources (1)

• [1]Formal-Verification-Guided Bayesian Optimization for Non-Intrusive Last-Mile Coverage Closure
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 12, 10:07 PM

  We present a non-intrusive workflow that closes pre-defined bins in an existing, human-authored functional coverage model; it does not synthesize verification objectives from the design specification.

  Last-mile functional coverage closure for modern chip designs remains costly because a mature UVM environment exposes many controls while direct hits on the remaining bins are sparse.

Extractive summary: sentences quoted from the sources.