ACETONE: Predictable programming framework for machine learning applications in safety-critical systems

Machine learning applications have been gaining considerable attention
in the field of safety-critical systems. Nonetheless, there is up to
now no accepted development process that reaches classical safety
confidence levels. This is the reason why we have developed a generic
programming framework called ACETONE that is compliant with safety
objectives (including traceability and WCET computation) for machine
learning. More practically, the framework generates C code from a
detailed description of off-line trained feed-forward deep neural
networks that preserves the semantics of the original trained model
and for which the WCET can be assessed with OTAWA. We have compared
our results with Keras2c and uTVM with static runtime on a realistic
set of benchmarks.