  Verification of deep neural networks has witnessed a recent surge of
interest, fueled by success stories in diverse domains and by abreast concerns
about safety and security in envisaged applications. Complexity and sheer size
of such networks are challenging for automated formal verification techniques
which, on the other hand, could ease the adoption of deep networks in safety-
and security-critical contexts.
  In this paper we focus on enabling state-of-the-art verification tools to
deal with neural networks of some practical interest. We propose a new training
pipeline based on network pruning with the goal of striking a balance between
maintaining accuracy and robustness while making the resulting networks
amenable to formal analysis. The results of our experiments with a portfolio of
pruning algorithms and verification tools show that our approach is successful
for the kind of networks we consider and for some combinations of pruning and
verification techniques, thus bringing deep neural networks closer to the reach
of formally-grounded methods.
