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.

板载嵌入式系统，例如航空电子和飞行控制，船上计算，太空航天飞机，智能运输系统和仪器的智能运输系统和仪器以及紧急设施以及车辆，在计算负载以及对TimingGuaineees的严格限制方面都面临着大大增加的计算值。涉及设计师的两个重要问题是有效的任务调度大约梅特实时限制和减少系统范围的可靠性可靠性的能源消耗。嵌入式系统中的50％的故障与运营温度密切相关。这些反过来又通过这些系统的计算组件的功耗来确定。这项研究旨在开发峰值限制的调度方法，以减轻这些可靠性问题。该项目追求峰值功率模型的开发，以准确描述分布式嵌入式系统中的功率约束，从而可以在高负载期间安全有效地使用可用的计算资源。这种方法的独特性在于它的重点是具有不同工作负载的分布式嵌入式系统。 电源知识分区（PAP）方案用于混合体系结构，包括处理器和可重新配置的组件，例如现场可编程门阵列（FPGA），为系统设计人员提供调整功能，以进行性能和系统任务的实时时间表。该项目研究了混合嵌入式系统中快速感知的任务分配算法的开发。