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功耗管理技术，并证明功耗管理对功耗效率的限制以及对延迟的不利影响。 系统级电源管理的当前技术水平仅限于在某个空闲时间段后关闭系统的部分，从而忽略了应用程序时序约束、运行时流量和应用程序使用信息。 这项工作将开发功率性能控制“旋钮”，使我们能够做出有效的在线决策。具体应用将包括RTOS中的功率感知资源调度、定时感知功率优化以及功率节省和应用服务质量之间的权衡（例如，我们的技术重点是解决两个关键问题：（a）延迟约束的功率优化，即，系统级功耗的最小化，其中对由于功率管理引起的系统等待时间的影响进行约束;以及（B）功率约束的性能优化，例如，在给定的功率预算内的系统级任务实现和调度。作为第一步，我们专注于在线电源管理算法的有效性和他们的效率的分析界限，通过开发的延迟increasesdue到电源管理的界限。 我们引入了竞争比的概念阿萨作为一个给定的电源管理算法对最佳功耗配置文件的性能如何的定量测量。接下来，我们在更广泛的系统级时序和功耗仿真引擎中验证这些分析边界，从而实现准确的性能仿真，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实现新的电源管理服务，通过协调调度和资源关闭.

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.