Abstract. Automatic formal verification techniques generally require exponential resources with respect to the number of primary inputs of a netlist. In this paper, we present several fully-automated techniques to enable maximal input reductions of sequential netlists. First, we present a novel min-cut based localization refinement scheme for yielding a safely overapproximated netlist with minimal input count. Second, we present a novel form of reparameterization: as a trace-equivalence preserving structural abstraction, which provably renders a netlist with input count at most a constant factor of register count. In contrast to prior research in reparameterization to offset input growth during symbolic simulation, we are the first to explore this technique as a structural transformation for sequential netlists, enabling its benefits to general verification flows. In particular, we detail the synergy between these input-reducing abstractions, and with other transformations such as retiming which -as with traditional localization approaches -risks substantially increasing input count as a byproduct of its register reductions. Experiments confirm that the complementary reduction strategy enabled by our techniques is necessary for iteratively reducing large problems while keeping both proof-fatal design size metrics -register count and input countwithin reasonable limits, ultimately enabling an efficient automated solution.
Localization refinement is also used synergistically with input reparameterization, to attain maximal input reduction in sequential netlists, using min-cut analysis in a structural manner REF .
Maximal Input Reduction of Sequential Netlists via Synergistic Reparameterization and Localization Strategies
CHARME
Computer Science