  Novel test selectors used in simulation-based verification have been shown to
significantly accelerate coverage closure regardless of the number of coverage
holes. This paper presents a configurable and highly-automated framework for
novel test selection based on neural networks. Three configurations of this
framework are tested with a commercial signal processing unit. All three
convincingly outperform random test selection with the largest saving of
simulation being 49.37% to reach 99.5% coverage. The computational expense of
the configurations is negligible compared to the simulation reduction. We
compare the experimental results and discuss important characteristics related
to the performance of the configurations.
