Cristin-resultat-ID: 1922393
Sist endret: 15. februar 2022, 14:08
NVI-rapporteringsår: 2021
Resultat
Vitenskapelig artikkel
2021

Prepare: Power-Aware Approximate Real-time Task Scheduling for Energy-Adaptive QoS Maximization

Bidragsytere:
  • Shounak Chakraborty
  • Sangeet Saha
  • Magnus Själander og
  • Klaus McDonald-Maier

Tidsskrift

ACM Transactions on Embedded Computing Systems
ISSN 1539-9087
e-ISSN 1558-3465
NVI-nivå 1

Om resultatet

Vitenskapelig artikkel
Publiseringsår: 2021
Publisert online: 2021
Trykket: 2021
Volum: 20
Hefte: 5s
Sider: 1 - 25
Artikkelnummer: 62
Open Access

Beskrivelse Beskrivelse

Tittel

Prepare: Power-Aware Approximate Real-time Task Scheduling for Energy-Adaptive QoS Maximization

Sammendrag

Achieving high result-accuracy in approximate computing (AC) based real-time applications without violating power constraints of the underlying hardware is a challenging problem. Execution of such AC real-time tasks can be divided into the execution of the mandatory part to obtain a result of acceptable quality, followed by a partial/complete execution of the optional part to improve accuracy of the initially obtained result within the given time-limit. However, enhancing result-accuracy at the cost of increased execution length might lead to deadline violations with higher energy usage. We propose Prepare, a novel hybrid offline-online approximate real-time task-scheduling approach, that first schedules AC-based tasks and determines operational processing speeds for each individual task constrained by system-wide power limit, deadline, and task-dependency. At runtime, by employing fine-grained DVFS, the energy-adaptive processing speed governing mechanism of Prepare reduces processing speed during each last level cache miss induced stall and scales up the processing speed once the stall finishes to a higher value than the predetermined one. To ensure on-chip thermal safety, this higher processing speed is maintained only for a short time-span after each stall, however, this reduces execution times of the individual task and generates slacks. Prepare exploits the slacks either to enhance result-accuracy of the tasks, or to improve thermal and energy efficiency of the underlying hardware, or both. With a 70 - 80% workload, Prepare offers 75% result-accuracy with its constrained scheduling, which is enhanced by 5.3% for our benchmark based evaluation of the online energy-adaptive mechanism on a 4-core based homogeneous chip multiprocessor, while meeting the deadline constraint. Overall, while maintaining runtime thermal safety, Prepare reduces peak temperature by up to 8.6 °C for our baseline system. Our empirical evaluation shows that constrained scheduling of Prepare outperforms a state-of-the-art scheduling policy, whereas our runtime energy-adaptive mechanism surpasses two current DVFS based thermal management techniques.

Bidragsytere

Shounak Chakraborty

  • Tilknyttet:
    Forfatter
    ved Institutt for datateknologi og informatikk ved Norges teknisk-naturvitenskapelige universitet

Sangeet Saha

  • Tilknyttet:
    Forfatter
    ved University of Essex
Aktiv cristin-person

Hans Magnus Själander

Bidragsyterens navn vises på dette resultatet som Magnus Själander
  • Tilknyttet:
    Forfatter
    ved Institutt for datateknologi og informatikk ved Norges teknisk-naturvitenskapelige universitet

Klaus McDonald-Maier

  • Tilknyttet:
    Forfatter
    ved University of Essex
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