  Deep neural network (DNN) verification is an emerging field, with diverse
verification engines quickly becoming available. Demonstrating the
effectiveness of these engines on real-world DNNs is an important step towards
their wider adoption. We present a tool that can leverage existing verification
engines in performing a novel application: neural network simplification,
through the reduction of the size of a DNN without harming its accuracy. We
report on the work-flow of the simplification process, and demonstrate its
potential significance and applicability on a family of real-world DNNs for
aircraft collision avoidance, whose sizes we were able to reduce by as much as
10%.
