SAT-based State Justification with Adaptive Mining of Invariants

We present a new approach to intelligently mine three types of invariants from a sequential circuit to significantly improve SAT-based state justification. We adaptively generate mining databases targeting on the hard-to-reach corner-case states, from which global invariants, target state related invariants, and observability-don't-care extended invariants are mined. Each mined invariant involves two or more signals that span across multiple time-frames, which capture the knowledge of the state spaces related to a target state. These invariants are then checked for their validity, and they can significantly increase the deductive power of the instance by pruning a larger portion of the search space. Experimental results show that more than an order of magnitude performance improvement can be obtained when justifying hard-to-justify states.