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Preprocessing SAT instances can reduce their size considerably. We combine variable elimination with subsumption and self-subsuming resolution, and show that these techniques not only shrink the formula further than previous preprocessing efforts based on variable elimination, but also decrease runtime of SAT solvers substantially for typical industrial SAT(More)
—Last spring, in March 2010, Aaron Bradley published the first truly new bit-level symbolic model checking algorithm since Ken McMillan's interpolation based model checking procedure introduced in 2003. Our experience with the algorithm suggests that it is stronger than interpolation on industrial problems , and that it is an important algorithm to study(More)
The introduction of symbolic model checking using Binary Decision Diagrams (BDDs) has led to a substantial extension of the class of systems that can be algorithmically veriied. Although BDDs have played a crucial role in this success, they have some well-known drawbacks, such as requiring an externally supplied variable ordering and causing space blowups(More)
The paper explores several ways to improve the speed and capacity of combinational equivalence checking based on Boolean satisfiability (SAT). State-of-the-art methods use simulation and BDD/SAT sweeping on the input side (i.e. proving equivalence of some internal nodes in a topological order), interleaved with attempts to run SAT on the output (i.e.(More)
This paper presents a lightweight sweeping method, similar to SAT-and BDD-sweeping. Performance are on the order of 10x to 100x faster than SAT-sweeping for large designs , while achieving about 50-90% of the reductions.
This paper presents an efficient, combined formulation of two widely used abstraction methods for bit-level verification: counterexample-based abstraction (CBA) and proof-based abstraction (PBA). Unlike previous work, this new method is formulated as a single, incremental SAT-problem, interleaving CBA and PBA to develop the abstraction in a bottom-up(More)
We study the effectiveness of neural sequence models for premise selection in automated theorem proving, one of the main bottlenecks in the formalization of mathematics. We propose a two stage approach for this task that yields good results for the premise selection task on the Mizar corpus while avoiding the hand-engineered features of existing(More)