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

Chakraborty, Shounak; Saha, Sangeet; Själander, Magnus; Mcdonald-Maier, Klaus

Abstract

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 multi-processor, 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.

Citation

Chakraborty, S., Saha, S., Själander, M., & Mcdonald-Maier, K. (2021). Prepare: Power-Aware Approximate Real-time Task Scheduling for Energy-Adaptive QoS Maximization. ACM Transactions on Embedded Computing Systems, 20(5s), 1-25. https://doi.org/10.1145/3476993

• Journal Article Type: Article
• Acceptance Date: Jul 1, 2021
• Online Publication Date: Sep 17, 2021
• Publication Date: Oct 31, 2021
• Deposit Date: Jan 9, 2025
• Journal: ACM Transactions on Embedded Computing Systems
• Print ISSN: 1539-9087
• Electronic ISSN: 1558-3465
• Publisher: Association for Computing Machinery (ACM)
• Peer Reviewed: Peer Reviewed
• Volume: 20
• Issue: 5s
• Article Number: 62
• Pages: 1-25
• DOI: https://doi.org/10.1145/3476993
• Public URL: https://durham-repository.worktribe.com/output/3328935