arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新,休息一下吧,周末愉快~~
arXiv 1602.04414cs.AR
收藏

嵌入式系统的温度感知动态优化

Temperature-aware Dynamic Optimization of Embedded Systems

Tosiron Adegbija, Ann Gordon-Ross

摘要:

由于嵌入式系统严格的设计约束,许多先前的工作集中在优化能耗和/或性能。由于嵌入式系统通常具有较少的冷却选项,因此温度升高以及温度优化是一个紧迫的问题。由于缺乏专用的热管理硬件机制,大多数嵌入式系统仅通过被动对流散热。嵌入式系统的温度不仅影响系统的可靠性,还可能影响系统的性能、功耗和成本。因此,嵌入式系统需要有效的热管理技术。然而,热管理可能与其他优化目标相冲突,例如执行时间和能耗。在本文中,我们专注于使用缓存优化和动态频率缩放的协同作用来管理温度,同时还优化了执行时间和能耗。本文提供了新的见解,高速缓存参数的影响,高效的温度感知的高速缓存调整算法。此外,我们提出了温度感知的基于阶段的调整,TaPT,它确定帕累托最优的时钟频率和高速缓存配置的细粒度的执行时间,能源和温度的权衡。TaPT可实现自主系统优化,还允许设计人员指定温度约束和优化优先级。实验结果表明,TaPT算法能够有效地减少执行时间、能耗和温度,同时使硬件开销最小。

英文摘要:

Due to embedded systems` stringent design constraints, much prior work focused on optimizing energy consumption and/or performance. Since embedded systems typically have fewer cooling options, rising temperature, and thus temperature optimization, is an emergent concern. Most embedded systems only dissipate heat by passive convection, due to the absence of dedicated thermal management hardware mechanisms. The embedded system`s temperature not only affects the system`s reliability, but could also affect the performance, power, and cost. Thus, embedded systems require efficient thermal management techniques. However, thermal management can conflict with other optimization objectives, such as execution time and energy consumption. In this paper, we focus on managing the temperature using a synergy of cache optimization and dynamic frequency scaling, while also optimizing the execution time and energy consumption. This paper provides new insights on the impact of cache parameters on efficient temperature-aware cache tuning heuristics. In addition, we present temperature-aware phase-based tuning, TaPT, which determines Pareto optimal clock frequency and cache configurations for fine-grained execution time, energy, and temperature tradeoffs. TaPT enables autonomous system optimization and also allows designers to specify temperature constraints and optimization priorities. Experiments show that TaPT can effectively reduce execution time, energy, and temperature, while imposing minimal hardware overhead.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑