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Inspired by recent successes of parallel techniques for solving Boolean satisfiability, we investigate a set of strategies and heuristics to leverage parallelism and improve the scalability of neural network verification. We present a general description of the Split-and-Conquer partitioning algorithm, implemented within the Marabou framework, and discuss its parameters and heuristic choices. In particular, we explore two novel partitioning strategies, that partition the input space or the phases of the neuron activations, respectively. We introduce a branching heuristic and a direction heuristic that are based on the notion of polarity. We also introduce a highly parallelizable pre-processing algorithm for simplifying neural network verification problems. An extensive experimental evaluation shows the benefit of these techniques on both existing and new benchmarks. A preliminary experiment ultra-scaling our algorithm using a large distributed cloud-based platform also shows promising results. 

Systems mixing Boolean logic and arithmetic have been a long-standing challenge for verification tools such as SAT-based bit-vector solvers. Though SAT solvers can be highly efficient for Boolean reasoning, they scale poorly once multiplication is involved. Algebraic methods using Gröbner basis reduction have recently been used to efficiently verify multiplier circuits in isolation, but generally do not perform well on problems involving bit-level reasoning. We propose that pseudo-Boolean solvers equipped with cutting planes reasoning have the potential to combine the complementary strengths of the existing SAT and algebraic approaches while avoiding their weaknesses. Theoretically, we show that there are optimal-length cutting planes proofs for a large class of bit-level properties of some well known multiplier circuits. This scaling is significantly better than the smallest proofs known for SAT and, in some instances, for algebraic methods. We also show that cutting planes reasoning can extract bit-level consequences of word-level equations in exponentially fewer steps than methods based on Gröbner bases. Experimentally, we demonstrate that pseudo-Boolean solvers can verify the word-level equivalence of adder-based multiplier architectures, as well as commutativity of bit-vector multiplication, in times comparable to the best algebraic methods. We then go further than previous approaches and also verify these properties at the bit-level. Finally, we find examples of simple nonlinear bit-vector inequalities that are intractable for current bit-vector and SAT solvers but easy for pseudo-Boolean solvers. 

Artificial Neural Networks (ANNs) have demonstrated remarkable utility in various challenging machine learning applications. While formally verified properties of their behaviors are highly desired, they have proven notoriously difficult to derive and enforce. Existing approaches typically formulate this problem as a post facto analysis process. In this paper, we present a novel learning framework that ensures such formal guarantees are enforced by construction. Our technique enables training provably correct networks with respect to a broad class of safety properties, a capability that goes well-beyond existing approaches, without compromising much accuracy. Our key insight is that we can integrate an optimization-based abstraction refinement loop into the learning process and operate over dynamically constructed partitions of the input space that considers accuracy and safety objectives synergistically. The refinement procedure iteratively splits the input space from which training data is drawn, guided by the efficacy with which such partitions enable safety verification. We have implemented our approach in a tool (ART) and applied it to enforce general safety properties on unmanned aviator collision avoidance system ACAS Xu dataset and the Collision Detection dataset. Importantly, we empirically demonstrate that realizing safety does not come at the price of much accuracy. Our methodology demonstrates that an abstraction refinement methodology provides a meaningful pathway for building both accurate and correct machine learning networks.