Alur, Rajeev

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  • Publication
    Verisig 2.0: Verification of Neural Network Controllers Using Taylor Model Preconditioning
    (2021-07-01) Ivanov, Radoslav; Carpenter, Taylor; Weimer, James; Alur, Rajeev; Pappas, George; Lee, Insup
    This paper presents Verisig 2.0, a verification tool for closed-loop systems with neural network (NN) controllers. We focus on NNs with tanh/sigmoid activations and develop a Taylor-model-based reachability algorithm through Taylor model preconditioning and shrink wrapping. Furthermore, we provide a parallelized implementation that allows Verisig 2.0 to efficiently handle larger NNs than existing tools can. We provide an extensive evaluation over 10 benchmarks and compare Verisig 2.0 against three state-of-the-art verification tools. We show that Verisig 2.0 is both more accurate and faster, achieving speed-ups of up to 21x and 268x against different tools, respectively.