  Implementing deep neural networks in safety critical systems, in particular
in the aeronautical domain, will require to offer adequate specification
paradigms to preserve the semantics of the trained model on the final hardware
platform. We propose to extend the nnef language in order to allow traceable
distribution and parallelisation optimizations of a trained model. We show how
such a specification can be implemented in cuda on a Xavier platform.
