In sharp contrast to conventional performance indicative based Network-on-Chip (NoC) DVFS, where the direct relation between application performance and NoC power consumption is missing, we exploit the concept of Performance-Power Characteristic Curve (PPCC) newly proposed in the literature to approach maximum NoC power efficiency. PPCC, which defines the direct relation between application performance and NoC power consumption, consists of three distinct regions: an inertial region due to power under-provisioning, a linear region for proportional performance gain, and a saturation region due to power over-provisioning. With PPCC as a guidance, we propose $\Delta$Δ-DVFS, which employs a “profile-then-select” strategy to step-by-step approach maximum NoC power efficiency. $\Delta$Δ-DVFS is built on two observations. First, in multi-threaded applications, maximum NoC power efficiency is achieved at the boundary between the linear region and the saturation region on the PPCC. Second, PPCC stabilizes when threads repeat workloads of the same loop. This is intuitively meaningful because loop repetition stresses NoC with similar workload. Based on the observations, $\Delta$Δ-DVFS uses the first several loop iterations for PPCC profiling. After the profiling is done, $\Delta$Δ-DVFS selects and applies the optimal V/F that achieves maximum NoC power efficiency to the remaining loop iterations. To accurately and timely follow PPCC when threads proceed to different loops, $\Delta$Δ-DVFS utilizes an H-tree loop monitor to detect loop change among distributive threads.

中文翻译：

---

通过 PPCC 引导的 NoC DVFS 追求极高的能效

与传统的基于性能指标的片上网络 (NoC) DVFS 形成鲜明对比，其中缺少应用程序性能和 NoC 功耗之间的直接关系，我们利用了文献中新提出的性能-功率特性曲线 (PPCC) 的概念以接近最大 NoC 电源效率。PPCC 定义了应用程序性能和 NoC 功耗之间的直接关系，它由三个不同的区域组成：由于功率供应不足导致的惯性区域、比例性能增益的线性区域以及由于功率过度供应导致的饱和区域。以政协为指导，我们提出$\Delta$Δ-DVFS，它采用“配置文件然后选择”策略逐步实现最大 NoC 功率效率。 $\Delta$Δ-DVFS 建立在两个观察的基础上。首先，在多线程应用中，最大的 NoC 功率效率是在 PPCC 上的线性区域和饱和区域之间的边界处实现的。其次，当线程重复同一循环的工作负载时，PPCC 会稳定。这在直观上很有意义，因为循环重复强调 NoC 的工作负载相似。根据观察，$\Delta$Δ-DVFS 使用前几个循环迭代进行 PPCC 分析。分析完成后，$\Delta$Δ-DVFS 选择最佳 V/F 并将其应用到剩余循环迭代中，以实现最大 NoC 功率效率。为了在线程进行不同循环时准确及时地跟踪 PPCC，$\Delta$Δ-DVFS 利用 H 树循环监视器来检测分布式线程之间的循环变化。