GLA: Gate-level abstraction revisited

Verification benefits from removing logic that is not relevant for a proof. Techniques for doing this are known as localization abstraction. Abstraction is often performed by selecting a subset of gates to be included in the abstracted model; the signals feeding into this subset become unconstrained cut-points. In this paper, we propose several improvements to substantially increase the scalability of automated abstraction. In particular, we show how a better integration between the BMC engine and the SAT solver is achieved, resulting in a new hybrid abstraction engine, that is faster and uses less memory. This engine speeds up computation by constant propagation and circuit-based structural hashing while collecting UNSAT cores for the intermediate proofs in terms of a subset of the original variables. Experimental results show improvements in the abstraction depth and size.