Power-Aware Resource Management in Densely Packaged Distributed Real-Time Embedded Systems

密集封装分布式实时嵌入式系统中的功耗感知资源管理

• 批准号: 0509483
• 负责人: Rabi Mahapatra
• 金额: --
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-01 至 2008-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0509483&HistoricalAwards=false
• 关键词: Power; Aware; Resource; Management; Densely

On-board embedded systems, such as those found in avionics and flight control,shipboard computing, space shuttles, intelligent transport systems and instrumentations inmedical and emergency facilities and vehicles, face greatly increased computationalrequirements both in terms of computation load as well as stringent constraints on timingguarantees. Two important issues that concern designers are efficient task scheduling tomeet real-time constraints and the reduction of system-wide energy consumption forincreased reliability.Over 50% of failures in embedded systems are closely related to operating temperatures.These in turn are determined by the power consumption of the computational componentsof the system. This research seeks to develop peak power constrained scheduling methods to alleviate these reliability concerns. The project pursues the development of a peak power model to accurately describe the power constraints in a distributed embedded system, thereby allowing for a safe and effective use of the available computational resources during periods of high load. The uniqueness of this approach is its focus on distributed embedded systems with varying workloads. Power aware partitioning (PAP) schemes for hybrid architectures comprising processors and reconfigurable components, such as a Field Programmable Gate Arrays (FPGA), provide system designers with tuning capabilities for performance and real-time scheduling of system tasks. The project investigates the development of fast power-aware task partitioning algorithms in hybrid embedded systems.

机载嵌入式系统，如航空电子和飞行控制、舰船计算、航天飞机、智能交通系统以及医疗和急救设施和车辆中的仪器，在计算负荷和时间保证方面都面临着极大的计算要求。设计人员关注的两个重要问题是满足实时约束的高效任务调度和为提高可靠性而降低系统能耗。嵌入式系统中50%以上的故障与工作温度密切相关，而这些又由系统计算部件的功耗决定。本研究旨在开发峰值功率受限的调度方法，以缓解这些可靠性问题。该项目致力于开发峰值功率模型，以准确描述分布式嵌入式系统中的功率约束，从而允许在高负载期间安全和有效地使用可用的计算资源。这种方法的独特之处在于它专注于具有不同工作负载的分布式嵌入式系统。用于包括处理器和可重配置组件的混合体系结构的功率感知分区(PAP)方案，例如现场可编程门阵列(FPGA)，为系统设计者提供了针对系统任务的性能和实时调度的调整能力。该项目研究了混合嵌入式系统中快速节能任务划分算法的发展。