Performance Tradeoff in DNN-based Coexisting Applications in Resource-Constrained Cyber-Physical Systems

Modern cyber-physical systems use deep-learning based algorithms for many applications for intelligent decision-making. Many of these systems are resource-constrained due to small form factor or finite energy budget. However, these systems often use multiple deep-learning algorithms simultaneously for a given mission or task. Due to the diverse nature of the algorithms and their performance needs, we need to allocate optimal software and hardware resources for their coexistence. To this aim, in this paper, we study and evaluate the performance tradeoff which will enable the users to choose the size and complexity of the deep learning models, the capacity of the device and also the software framework. With real-world experiments with a wide range of hardware and software, we demonstrate and evaluate the performance of the coexisting deep neural networks (DNN) based applications.