Research directions for real-time implementation of AI algorithms

The embedding of AI algorithms in critical systems faces significant scientific issues, among these issues, guaranteeing the temporal determinism of AI algorithms. Real-time scheduling theory can help addressing this challenge but necessitate the proposition of new real-time task models and scheduling algorithms. In this context, we identify three problems to address, firstly, the proposition of new task models for AI algorithms that depends on the class of AI algorithms to which they belong, secondly, how to integrate the architecture characteristics into the real-time task model, and finally, the establishment of benchmarks that are specific to the evaluation of the execution of AI algorithms with real-time constraints. To illustrate our statement, in this work we focus on the problem of the implementation of convolutional neural networks (CNNs) during the inference phase executed on NVIDIA GPUs.