Constrained Power and Performance Optimization for Embedded Systems

嵌入式系统的受限功耗和性能优化本研究的目标是探索嵌入式系统的电源管理技术，该技术具有可证明的电源效率界限以及电源管理对延迟的不利影响。当前技术水平的系统级电源管理仅限于在特定空闲时间段之后关闭系统的部分，从而忽略应用定时约束、运行时流量和应用使用信息。这项工作将开发动力性能控制“旋钮”，使我们能够做出有效的在线决策。具体的应用包括RTOS中的功耗感知资源调度、时序感知功耗优化以及节能和应用服务质量之间的权衡(例如，错过截止日期)。我们的技术重点是解决两个关键问题：(A)延迟受限的功耗优化，即通过限制由于电源管理造成的系统延迟的影响来最小化系统级功耗；(B)功耗受限的性能优化，例如，在给定的功率预算内，系统级任务的实施和调度。作为第一步，我们专注于在线电源管理算法的有效性及其效率的分析界，通过开发由于电源管理而导致的延迟增加的界。我们引入了竞争比的概念，作为对给定功率管理算法相对于最优功耗配置的执行情况的定量度量。接下来，我们将这些分析界限合并到一个更广泛的系统级时序和功率模拟引擎中，该引擎可以实现准确的性能模拟，同时最大限度地减少与实际系统功能相关的细节。我们的实验评估是通过RTOS通过协调调度和资源关闭来实现新的电源管理服务。

Constrained Power and Performance Optimization for Embedded SystemsThe goal of this research is to explore techniques for power management ofembedded systems with provable bounds on power efficiency as well as adverseeffects on latency due to power management. The current state of the art insystem-level power management is limited to shutting parts of a system aftera certain period of idle time, thus ignoring the application timingconstraints, runtime traffic and application usage information. This workwill develop power-performance control "knobs" that will allow us to makeeffective online decisions. Specific applications will include power-awareresource scheduling in RTOS, timing-aware power optimizations and tradeoffsbetween power savings and application quality of service (e.g., misseddeadlines).Our technical focus is on solving two key problems: (a) latency-constrainedpower optimization, i.e., minimization of system-level power consumptionwith constrains on the effect of system latency due to power management; and(b) power-constrained performance optimization, e.g., system-level taskimplementation and scheduling within a given power budget. As a first step,we focus on analytic bounds on the effectiveness of online power managementalgorithms and their efficiency by developing bounds on latency increasesdue to power management. We introduce the notion of a competitive ratio asa quantitative measure of how well a given power management algorithmperforms against an optimum power consumption profile. Next, we incorporatethese analytic bounds in a broader system-level timing and power simulationengine which enables an accurate performance simulation while minimizing thedetails related to actual system functionality. Our experimental evaluationis through RTOS implementation of new power management services throughcoordinated scheduling and resource shutdown.