Deep Neural Networks (DNNs) are the fundamental structure adopted to provide smart services in a wide range of AI applications. However, DNN-based tasks have high computing requirements, posing huge challenges on their deployment on small and resource constrained devices such as mobile phones or IoT devices. To address this issue, some solutions consider model compression techniques to limit the computational burden of the device as well as the model memory footprint. Other strategies involve partial or full task offloading towards more powerful computing platforms placed at the edge of new-generation mobile networks (5G-MEC), ensuring low latency and near-zero computing cost on the mobile device.