A Comparative Evaluation of Latency-Aware Energy Optimization Approaches in Many-Core Systems (Invited Paper)

Authors Khalil Esper , Stefan Wildermann , Jürgen Teich

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Khalil Esper
  • Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Germany
Stefan Wildermann
  • Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Germany
Jürgen Teich
  • Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Germany

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Khalil Esper, Stefan Wildermann, and Jürgen Teich. A Comparative Evaluation of Latency-Aware Energy Optimization Approaches in Many-Core Systems (Invited Paper). In Second Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2021). Open Access Series in Informatics (OASIcs), Volume 87, pp. 1:1-1:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)


Many applications vary a lot in execution time depending on their workload. A prominent example is image processing applications, where the execution time is dependent on the content or the size of the processed input images. An interesting case is when these applications have quality-of-service requirements such as soft deadlines, that they should meet as good as possible. A further complicated case is when such applications have one or even multiple further objectives to optimize like, e.g., energy consumption. Approaches that dynamically adapt the processing resources to application needs under multiple optimization goals and constraints can be characterized into the application-specific and feedback-based techniques. Whereas application-specific approaches typically statically use an offline stage to determine the best configuration for each known workload, feedback-based approaches, using, e.g., control theory, adapt the system without the need of knowing the effect of workload on these goals. In this paper, we evaluate a state-of-the-art approach of each of the two categories and compare them for image processing applications in terms of energy consumption and number of deadline misses on a given many-core architecture. In addition, we propose a second feedback-based approach that is based on finite state machines (FSMs). The obtained results suggest that whereas the state-of-the-art application-specific approach is able to meet a specified latency deadline whenever possible while consuming the least amount of energy, it requires a perfect characterization of the workload on a given many-core system. If such knowledge is not available, the feedback-based approaches have their strengths in achieving comparable energy savings, but missing deadlines more often.

Subject Classification

ACM Subject Classification
  • Hardware → Power and energy
  • Hardware → Finite state machines
  • Computing methodologies → Computational control theory
  • Computer systems organization → Self-organizing autonomic computing
  • energy optimization
  • control-theory
  • timing analysis
  • soft real-time
  • dynamic voltage and frequency scaling
  • finite state machines
  • multi-core
  • many-core


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