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GLA: Gate-Level Abstraction Revisited
Alan Mishchenko Niklas Een Robert Brayton Jason Baumgartner Hari Mony Pradeep Nalla
Department of EECS, University of California, Berkeley IBM Systems and Technology Group
{alanmi, brayton}@ niklas@een.se {baumgarj, harimony}@ pranalla@
Abstract memory, compared to previous work based on complete
proof-logging; (b) a new abstraction refinement framework,
Verification benefits from removing logic that is not which reduces the number of refinement iterations; and (c)
relevant for a proof. Techniques for doing this are known as a new feature called rollback, which allows the SAT solver
localization abstraction. Abstraction is often performed by to return to a previously marked point in solving a problem.
selecting a subset of gates to be included in the abstracted To understand the importance of the main contribution,
model; the signals feeding into this subset become we start by introducing hybrid abstraction (HA) presented
unconstrained cut-points. In this paper, we propose several in [10] and outline the two key insights that led to the gate-
improvements to substantially increase the scalability of level abstraction (GLA) described in the current paper.
automated abstraction. In particular, we show how a better The implementation of HA constructs a localization
integration between the BMC engine and the SAT solver is abstraction by iteratively computing (1) counterexamples
achieved, resulting in a new hybri
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