Determining Application-specific Peak Power and Energy Requirements for Ultra-low Power Processors
Determining Application-specific Peak Power and Energy Requirements for Ultra-low Power Processors
复制标题
确定超低功耗处理器的特定应用峰值功率和能量要求
DOI：
10.1145/3037697.3037711
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
J. Sartori
中科院分区：
文献类型：
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作者：
Hari Cherupalli;Henry Duwe;Weidong Ye;Rakesh Kumar;J. Sartori
物联网，可穿戴设备，植入物和传感器网络等许多新兴应用都受到电力和能量约束。这些应用依赖于迅速成为当今生产最丰富的处理器的超低功率处理器。在这些应用使用的超低功率嵌入式系统中，峰值功率和能量需求是决定关键系统特征（例如大小，重量，成本和寿命）的主要因素。尽管这些系统的功率和能源需求往往是特定于应用的，但评级峰值功率和能量的常规技术不能准确地绑定在处理器上运行的应用程序的功率和能量需求，从而导致过度加权，从而增加了系统尺寸和重量。在本文中，我们提出了一种自动化技术，该技术在嵌入式系统中对应用程序和超低功率处理器进行硬件软件共分析，以确定特定于应用程序的峰值功率和能源需求。与传统技术相比，我们的技术提供了比常规技术更准确，更紧密的界限，比基于分析和防护键的传统方法，平均峰值功率低15％，峰值能量降低了17％。与基于激进的压力标记方法相比，我们的技术报告的功率和能量界限平均分别降低了26％和26％。同样，与传统方法不同，我们的技术报告保证了与应用程序输入集无关的峰值功率和能量的界限。可以利用峰值功率和能量的更紧密的界限，以降低系统尺寸，重量和成本。
Many emerging applications such as IoT, wearables, implantables, and sensor networks are power- and energy-constrained. These applications rely on ultra-low-power processors that have rapidly become the most abundant type of processor manufactured today. In the ultra-low-power embedded systems used by these applications, peak power and energy requirements are the primary factors that determine critical system characteristics, such as size, weight, cost, and lifetime. While the power and energy requirements of these systems tend to be application-specific, conventional techniques for rating peak power and energy cannot accurately bound the power and energy requirements of an application running on a processor, leading to over-provisioning that increases system size and weight. In this paper, we present an automated technique that performs hardware-software co-analysis of the application and ultra-low-power processor in an embedded system to determine application-specific peak power and energy requirements. Our technique provides more accurate, tighter bounds than conventional techniques for determining peak power and energy requirements, reporting 15% lower peak power and 17% lower peak energy, on average, than a conventional approach based on profiling and guardbanding. Compared to an aggressive stressmark-based approach, our technique reports power and energy bounds that are 26% and 26% lower, respectively, on average. Also, unlike conventional approaches, our technique reports guaranteed bounds on peak power and energy independent of an application's input set. Tighter bounds on peak power and energy can be exploited to reduce system size, weight, and cost.