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PATMOS
2015
Springer

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

2 years 11 months ago
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...
Sheng Yang, Rishad A. Shafik, Geoff V. Merrett, Ed
Added 16 Apr 2016
Updated 16 Apr 2016
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
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