Heterogeneous Secure Multi-level Remote Acceleration Service for Low-power Integrated Systems and Devices
Lara López, Francisco Javier Nieto, Terpsichori-Helen Velivassaki, Sokol Kosta, Cheol-Ho Hong, Raffaele Montella, Iakovos Mavroidis, Carles Fernández–Prades · Procedia Computer Science · 2016
This position paper presents a novel heterogeneous CPU-GPU multi-level cloud acceleration focusing on applications running on embedded systems found on low-power devices. A runtime system performs energy and performance estimations in order to automatically select local CPU-based and GPU-based tasks that should be seamlessly executed on more powerful remote devices or cloud infrastructures. Moreover, it proposes, for the first time, a secure unified model where almost any device or infrastructure can operate as an accelerated entity and/or as an accelerator serving other less powerful devices in a secure way.