Challenging SMT solvers to verify neural networks

Abstract

In recent years, Satisfiability Modulo Theory (SMT) solvers are becoming increasingly popular in the Computer Aided Verification and Reasoning community. Used natively or as back-engines, they are accumulating a record of success stories and, as witnessed by the annual SMT competition, their performances and capacity are also increasing steadily. Introduced in previous contributions of ours, a new application domain providing an outstanding challenge for SMT solvers is represented by verification of Multi-Layer Perceptrons (MLPs) a widely-adopted kind of artificial neural network. In this paper we present an extensive evaluation of the current state-of-the-art SMT solvers and assess their potential in the promising domain of MLP verification.