Adaptive energy minimization of embedded heterogeneous systems using regression-based learning

—Modern embedded systems consist of heterogeneous computing resources with diverse energy and performance trade-offs. This is because the computing resources exercise the application tasks differently, generating varying workloads and energy consumption. As a result, minimizing energy consumption in these systems is challenging as it requires continuous adaptation of application task mapping (i.e. allocating tasks among the computing resources) and dynamic voltage/frequency scaling (DVFS). Existing approaches lack such adaptation with practical validation (Table I). This paper proposes a novel adaptive energy minimization approach for embedded heterogeneous systems. Fundamental to this approach is a runtime model, generated through regression-based learning of energy/performance trade-offs between different computing resources in the system. Using this model, an application task is suitably mapped on a computing resource during runtime, ensuring minimum energy consumption for a given...

• Type: Journal
• Year: 2015
• Where: PATMOS
• Authors: Sheng Yang, Rishad A. Shafik, Geoff V. Merrett, Edward A. Stott, Joshua M. Levine, James J. Davis, Bashir M. Al-Hashimi
• Keywords: Modeling And Simulation, PATMOS 2015