CSR: Small: Collaborative Research:Exploiting Predictability & Interdependency of Physical Parameters for Resource-Efficient Integration of Real-Time Embedded Systems

日常活动越来越多地由嵌入式系统实现。在这种系统中，违反最后期限可能会导致危险和/或灾难性的后果。这些系统的正时参数由这些系统的物理状态驱动(例如，汽车系统中的点火正时由发动机转速决定)。当前处理物理环境状态和定时参数之间的依赖关系的方法进行非常简单的假设，导致硬件和能源资源的浪费。这项研究有助于更有效地集成嵌入式系统，从而通过结合机械工程、控制系统和实时调度技术来实现更小的技术。由此产生的技术将最大限度地减少无处不在、运行在我们日常生活中的系统的碳足迹。该项目的教育目标是将生成的人工制品纳入本科和研究生水平的课程，以及通过夏令营和挑战课程在当地学校的K-12推广中。该研究旨在通过分析、建模和预测物理状态与时间参数之间的依赖关系，将实时嵌入式系统的计算利用率降至最低，以减少下一代嵌入式系统的大小、重量和功率需求。一个中心目标是为表征系统的多个物理维度及其相互依赖的新的实时任务模型开发运行时调度算法和相关的可调度性分析。当物理参数的估计不准确时，运行时自适应协议有效地分配资源。调度和分配算法在嵌入式平台上的实现提供了与传统单维模型相比的多维技术的性能评估。

Everyday activities are increasingly enabled by embedded systems. In such systems, a deadline violation can lead to dangerous and/or disastrous consequences. The timing parameters of such systems are driven by the physical state of these systems (e.g., ignition timing in automotive systems is determined by engine speed). Current approaches to deal with the dependencies between the physical environment state and timing parameters make very simplistic assumptions, resulting in wasted hardware and energy resources. This research facilitates more effective integration of embedded systems --thus enabling smaller technology via incorporation of techniques from mechanical engineering, control systems, and real-time scheduling. The resultant techniques will minimize the carbon footprint of systems that are ubiquitous and run our everyday lives. The educational objective of this project incorporates resulting artifacts into undergraduate- and graduate-level courses, as well as in K-12 outreach at local schools via summer camps and challenge sessions.This research aims to minimize the computational utilization of real-time embedded systems by analyzing, modeling, and predicting dependencies between the physical state and timing parameters to reduce the size, weight, and power requirements of next-generation embedded systems. A central objective is development of run-time scheduling algorithms and associated schedulability analysis for new real-time task models that characterize multiple physical dimensions of the system and their interdependencies. Run-time adaptation protocols effectively allocate resources when the estimates of the physical parameters are inaccurate. Implementations of the scheduling and allocation algorithms upon embedded platforms provide evaluation of performance of multi-dimensional techniques compared to the traditional single-dimensional models.