RePAiR: A Strategy for Reducing Peak Temperature while Maximising Accuracy of Approximate Real-Time Computing: Work-in-Progress

Shounak Chakraborty, S. Saha, Magnus Själander, K. Mcdonald-Maier
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Abstract

Improving accuracy in approximate real-time computing without violating thermal-energy constraints of the underlying hardware is a challenging problem. The execution of approximate real-time tasks can individually be bifurcated into two components: (i) execution of the mandatory part of the task to obtain a result of acceptable quality, followed by (ii) partial/complete execution of the optional part, which refines the initially obtained result, to increase the accuracy without violating the temporal-deadline. This paper introduces RePAiR, a novel task-allocation strategy for approximate real-time applications, combined with fine-grained DVFS and on-line task migration of the cores and power-gating of the last level cache, to reduce chip-temperature while respecting both deadline and thermal constraints. Furthermore, gained thermal benefits can be traded against system-level accuracy by extending the execution-time of the optional part.
修复:降低峰值温度同时最大限度地提高近似实时计算精度的策略:正在进行的工作
在不违反底层硬件的热能约束的情况下提高近似实时计算的精度是一个具有挑战性的问题。近似实时任务的执行可以单独分为两个部分:(i)执行任务的强制性部分,以获得质量可接受的结果;(ii)部分/完全执行可选部分,对初始获得的结果进行细化,在不违反时间截止日期的情况下提高精度。本文介绍了RePAiR,一种用于近似实时应用的新型任务分配策略,结合了细粒度DVFS和内核的在线任务迁移以及最后一级缓存的功率门控,以降低芯片温度,同时尊重截止日期和热约束。此外,可以通过延长可选部件的执行时间来牺牲获得的热收益和系统级精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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