LAUSR

Constrained random stimulus generation is no longer sufficient to fully simulate the functionality of a digital design. The increasing complexity of today’s hardware devices must be supported by powerful development and simulation environments, powerful computational mechanisms, and appropriate software to exploit them. Reinforcement learning, a powerful technique belonging to the field of artificial intelligence, provides the means to efficiently exploit computational resources to find even the least obvious correlations between configuration parameters, stimuli applied to digital design inputs, and their functional states. This paper, in which a novel software system is used to simplify the analysis of simulation outputs and the generation of input stimuli through reinforcement learning methods, provides important details about the setup of the proposed method to automate the verification process. By understanding how to properly configure a reinforcement algorithm to fit the specifics of a digital design, verification engineers can more quickly adopt this automated and efficient stimulus generation method (compared with classical verification) to bring the digital design to a desired functional state. The results obtained are most promising, with even 52 times fewer steps needed to reach a target state using reinforcement learning than when constrained random stimulus generation was used.