Coarse-Grained Computation-Oriented Energy Modeling for Heterogeneous Parallel Embedded Systems

Adam Seewald*, Ulrik Pagh Schultz, Emad Ebeid, Henrik Skov Midtiby

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Limited energy availability is among the most challenging considerations developers face for heterogeneous systems and is critical for battery-powered devices. For complex systems composed of mechanical and computational units, such as drones and mobile robots, more than half of the power consumption can be due to the computational operations. Critically, these systems are often composed of many components, interacting concurrently to achieve specific functionality. As a result, power prediction and estimation can be a challenging task, especially if different computational units, such as CPU and GPU, should be modeled. In this paper, we focus on limited energy availability for mobile heterogeneous devices powered by a battery and present a coarse-grained computation-oriented energy modeling approach. Our approach predicts the energy consumption of a set of software components, in a specific configuration, executed according to a given scheduling policy. The model, determined numerically from several empirical power samples, describes the energy consumed by a software configuration and can be used for energy-aware planning and optimization from a computational point of view. It can potentially support a complex embedded system in maximizing the level of autonomy while minimizing power consumption and preserving the most appropriate amount of battery charge by finding the right rate of quality of service. Our approach is supported and validated by the design and implementation of a profiling tool. The tool abstracts computational energy behavior and describes the current battery drain as a function of all the admissible configurations.

Original languageEnglish
JournalInternational Journal of Parallel Programming
Issue number2
Pages (from-to)136-157
Publication statusPublished - Apr 2021


  • Energy profiling
  • Energy modeling
  • Embedded platforms
  • Heterogeneous computing


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