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.

翻译：在安全关键系统（特别是航空领域）中实现深度神经网络时，需要提供适当的规范范式，以在最终硬件平台上保持训练模型的语义完整性。我们提出扩展NNEF语言，以实现训练模型的可追踪分布式与并行化优化。本文展示了如何在Xavier平台上通过CUDA实现此类规范。