一句话：提出软硬件协同自动分析技术，精确确定应用特定的超低功耗处理器峰值功率与能量需求，给出更紧且输入无关的保证边界。
IoT、可穿戴、植入式和传感器网络等新兴应用受功率与能量严格约束，依赖超低功耗处理器。其峰值功率与能量需求决定系统尺寸、重量、成本和寿命，但传统基于 profiling 与 guardbanding 或 stressmark 的评估无法准确界定应用特定需求，导致过度配置。本文提出自动化软硬件协同分析技术，对应用与超低功耗处理器联合分析，确定应用特定的峰值功率与能量需求，并给出与输入集无关的保证边界。实验表明，该技术相比传统 profiling+guardbanding 平均降低峰值功率 15%、峰值能量 17%；相比激进 stressmark 方法均降低 26%。更紧的边界有助于减小系统尺寸、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.
@inproceedings{CherupalliDY0S17,
title = {Determining Application-specific Peak Power and Energy Requirements for Ultra-low Power Processors},
author = {Hari Cherupalli and Henry Duwe and Weidong Ye and Rakesh Kumar and John Sartori},
booktitle = {ASPLOS},
year = {2017},
doi = {10.1145/3037697.3037711}
}