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，舰载计算，航天飞机，智能运输系统和医疗和应急设施和车辆的仪器仪表，面临着大大增加的计算要求，无论是在计算负载方面，以及严格的约束时间保证。设计人员关心的两个重要问题是有效的任务调度以满足实时约束和减少系统范围内的能量消耗以提高可靠性。嵌入式系统中超过50%的故障与工作温度密切相关。这些反过来又取决于系统计算组件的功耗。本研究旨在开发峰值功率约束调度方法，以减轻这些可靠性问题。该项目致力于开发一个峰值功率模型，以准确描述分布式嵌入式系统中的功率约束，从而在高负载期间安全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。 用于包括处理器和诸如现场可编程门阵列（FPGA）的可重新配置组件的混合架构的功率感知分区（PAP）方案为系统设计者提供了用于系统任务的性能和实时调度的调谐能力。本计画主要研究混合式嵌入式系统中之快速省电任务分割演算法。