ETAP：间歇性程序的能量感知时序分析
ETAP: Energy-aware Timing Analysis of Intermittent Programs
摘要：
能量收集无电池嵌入式设备仅依赖于环境能量收集，可实现独立和可持续的物联网应用。这些设备在其能量储存器中收集的环境能量足以操作时执行程序，否则突然停止执行（并开始充电）。这些间歇性程序在不同的能量条件、硬件配置和程序结构下具有不同的定时行为。本文提出了间歇性程序的能量感知时序分析（ETAP），一种概率符号执行方法，分析间歇性程序在编译时的时序和能量行为。ETAP象征性地执行给定的程序，同时考虑环境能量和动态能量消耗的时间和能量成本模型。我们评估ETAP几个间歇性的程序，并比较编译时的分析结果与实际硬件上的执行。结果表明，ETAP的归一化预测准确率为99.5%，与人工测试相比，它将时序分析速度提高了至少两个数量级。
英文摘要：
Energy harvesting battery-free embedded devices rely only on ambient energy harvesting that enables stand-alone and sustainable IoT applications. These devices execute programs when the harvested ambient energy in their energy reservoir is sufficient to operate and stop execution abruptly (and start charging) otherwise. These intermittent programs have varying timing behavior under different energy conditions, hardware configurations, and program structures. This paper presents Energy-aware Timing Analysis of intermittent Programs (ETAP), a probabilistic symbolic execution approach that analyzes the timing and energy behavior of intermittent programs at compile time. ETAP symbolically executes the given program while taking time and energy cost models for ambient energy and dynamic energy consumption into account. We evaluated ETAP on several intermittent programs and compared the compile-time analysis results with executions on real hardware. The results show that ETAP's normalized prediction accuracy is 99.5%, and it speeds up the timing analysis by at least two orders of magnitude compared to manual testing.
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