  Machine learning techniques often lack formal correctness guarantees,
evidenced by the widespread adversarial examples that plague most deep-learning
applications. This lack of formal guarantees resulted in several research
efforts that aim at verifying Deep Neural Networks (DNNs), with a particular
focus on safety-critical applications. However, formal verification techniques
still face major scalability and precision challenges. The over-approximation
introduced during the formal verification process to tackle the scalability
challenge often results in inconclusive analysis. To address this challenge, we
propose a novel framework to generate Verification-Friendly Neural Networks
(VNNs). We present a post-training optimization framework to achieve a balance
between preserving prediction performance and verification-friendliness. Our
proposed framework results in VNNs that are comparable to the original DNNs in
terms of prediction performance, while amenable to formal verification
techniques. This essentially enables us to establish robustness for more VNNs
than their DNN counterparts, in a time-efficient manner.
