Prepare: P owe r -Awar e A p proximate Re a l-time Task Scheduling for Ene r gy-Adaptiv e QoS Maximization

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.

Optimal IC Task Mapping to Maximize QoS on Heterogeneous Multicore Systems

“…Energy efficiency and real-time execution are typically required during the embedded system design because 1) embedded systems have energy constraints, especially when they are powered by the battery, which has limited energy capacity, and 2) real-time responsiveness is required by many critical applications (e.g., target tracking or fire detection) since missing the application's deadline would lead to serious or even disastrous consequences. In several application domains, such as image processing, robot control, and information gathering, a task can be logically decomposed into a mandatory subtask and an optional subtask [2]. The mandatory subtask must be completed before the deadline, providing a baseline QoS.…”

“…Therefore, they do not explore any QoS improvement by adjusting the optional cycles. On the contrary, the QoS-aware task mapping approaches [2], [3], [6]- [10] use the IC task model to maximize system QoS under real-time and/or energy constraints. For modeling the system QoS, the most realistic approaches are based on the linear function [2], [3], [6], [7] and the concave function [8]- [10].…”

“…On the contrary, the QoS-aware task mapping approaches [2], [3], [6]- [10] use the IC task model to maximize system QoS under real-time and/or energy constraints. For modeling the system QoS, the most realistic approaches are based on the linear function [2], [3], [6], [7] and the concave function [8]- [10]. The concave function is more general since it can characterize more extensive applications than the linear function [11].…”