论文标题
在异质平台中用于资源管理的能源感知的在线学习框架
An Energy-Aware Online Learning Framework for Resource Management in Heterogeneous Platforms
论文作者
论文摘要
移动平台必须满足快速响应时间和最小能耗的矛盾要求，这是动态变化的应用程序的函数。为了满足这一需求，这些设备核心的芯片系统（SOC）提供了各种控制旋钮，例如活动芯的数量及其电压/频率水平。在运行时最佳地控制这些旋钮是有挑战性的，原因有两个。首先，较大的配置空间禁止详尽的解决方案。其次，由于设计时间未知许多潜在的新应用程序，因此设计的控制策略充其量是最佳的。我们通过提出一种在线模仿学习方法来应对这些挑战。我们的关键想法是构建离线政策，并将其在线调整到新应用程序中，以优化给定的指标（例如能源）。所提出的方法利用了在运行时学到的力量绩效模型来实现的监督。我们在具有16个不同基准的商业移动平台上展示了其有效性。我们的方法在执行不到25％的说明后，成功地将控制策略适应了未知应用程序。
Mobile platforms must satisfy the contradictory requirements of fast response time and minimum energy consumption as a function of dynamically changing applications. To address this need, system-on-chips (SoC) that are at the heart of these devices provide a variety of control knobs, such as the number of active cores and their voltage/frequency levels. Controlling these knobs optimally at runtime is challenging for two reasons. First, the large configuration space prohibits exhaustive solutions. Second, control policies designed offline are at best sub-optimal since many potential new applications are unknown at design-time. We address these challenges by proposing an online imitation learning approach. Our key idea is to construct an offline policy and adapt it online to new applications to optimize a given metric (e.g., energy). The proposed methodology leverages the supervision enabled by power-performance models learned at runtime. We demonstrate its effectiveness on a commercial mobile platform with 16 diverse benchmarks. Our approach successfully adapts the control policy to an unknown application after executing less than 25% of its instructions.
