Using DNNs and smart sampling for coverage closure acceleration

Abstract

Coverage Directed Generation represents algorithms that are used to create tests or test-templates for hitting coverage events. Standard approaches for solving the problem use either user's intuition or random sampling. Recent work has been using optimization algorithms in order to hit, a single hard-to-hit event. In this work we extend the optimization technique for many events and show that by using a deep neural network one can accelerate the optimization significantly. The algorithms are presented on the NorthStar simulator where we show substantial improvement over random based techniques and a factor larger than 2 on other optimization-based techniques.