Temperature Effect Inversion-Aware Power-Performance Optimization for FinFET-Based Multicore Systems
Temperature Effect Inversion-Aware Power-Performance Optimization for FinFET-Based Multicore Systems
复制标题
基于 FinFET 的多核系统的温度影响反转感知功率性能优化
DOI：
10.1109/tcad.2017.2666721
复制
发表时间：
2017
影响因子：
2.9
通讯作者：
Diana Marculescu
中科院分区：
文献类型：
--
作者：
E. Cai;Diana Marculescu
能量和温度是现代高性能多核系统的主要限制。为了节省功耗或提高性能，动态电压和频率缩放 (DVFS) 广泛应用于几乎所有计算系统。随着 CMOS 技术不断缩小尺寸，FinFET 最近已成为多核系统的常见选择。与平面 CMOS 相比，FinFET 的特点是在超阈值电压区域的较高温度下延迟较低，这种效应称为温度效应反转 (TEI)。本文探讨了多核系统的 TEI 感知性能改进和节能。我们的实验结果表明，与 TEI 不可知的策略相比，TEI 感知的 DVFS 策略在稳定状态下平均可以实现 15.70% 的吞吐量提高或 31.26% 的节能。通过进一步研究，观察到由 TEI 效应产生的多个最佳点 (SS)。基于这些 SS 操作机制，本文介绍了提供等功率最大性能或等性能最小能耗的快速算法。实验结果证实了所提出方法的有效性，与最先进的算法相比，速度提高了 <inline-formula> <tex-math notation="LaTeX">$45.9 \times $ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">$55.3 \times $ </tex-math></inline-formula>所达到的绩效或能量分别为 0.22% 或 0.68%。
Energy and temperature are the main constraints for modern high-performance multicore systems. To save power or increase performance, dynamic voltage and frequency scaling (DVFS) is widely applied in literally all computing systems. As CMOS technology continues scaling, FinFET has recently become the common choice for multicore systems. In contrast with planar CMOS, FinFET is characterized by lower delay under higher temperatures in super-threshold voltage region, an effect called temperature effect inversion (TEI). This paper explores TEI-aware performance improvement and energy savings for multicore systems. Our experimental results show that on average 15.70% throughput improvement or 31.26% energy savings can be achieved in steady state by a TEI-aware DVFS policy over a TEI-agnostic one. By further investigation, multiple sweet spots (SSs) resulting from TEI effects are observed. Based on these SS operation regimes, this paper introduces fast algorithms which provide iso-power maximum performance or iso-performance minimum energy consumption. Experimental results confirm the effectiveness of the proposed approach by exhibiting a <inline-formula> <tex-math notation="LaTeX">$45.9 \times $ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">$55.3 \times $ </tex-math></inline-formula> speedup when compared to state-of-the-art algorithms while losing only 0.22% or 0.68% in achieved performance or energy, respectively.