J. Luo and N. Jha, ""Battery-Aware Static Scheduling for Distributed Real-Time Embedded Systems"," in IEEE/ACM Design Automation Conference, 2001.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....for DVS processing elements. Their approach is based on a DVS algorithm which keeps communication events fixed, i.e. they reduce the global optimization problem to smaller local problems which can be solved easier and 403 faster. Furthermore, they take battery characteristic into consideration [30] to increase the battery life time of the system. The approach is very efficient in terms of optimization time and achieved energy reduction, when the used processing elements show similar power consumption. However, in the case that the architecture is built out of highly heterogeneous PEs, this ....

Luo, J., and N. K. Jha. Battery-Aware Static Scheduling for Distributed Real-Time Embedded Systems. In Proc. IEEE 38th Design Automation Conf. (DACO1), 2001, pp. 444 449.

....for hard real time systems targeting a reduced power solution [14] Their method yields power reduction by exploiting slack inherent in the system schedule and those arising from variations of execution times. Luo and Jha developed a scheduling technique for distributed real time embedded systems [8]. Their scheduling technique performs variable voltage scheduling via efficient slack time re allocation to reduce average discharge power. Zhu et al. 5] describe a scheduling technique to reclaim the time unused by a task to reduce the execution speed of future tasks. Fields et al. propose a ....

J. Luo and N. Jha. "Battery-Aware Static Scheduling for Distributed Real-Time Embedded Systems". In Design Automation Conference, 2001.

....in the area of high level synthesis where timing slack of the nodes in the data flow graphs are considered for better optimization in area and power. Examples are the algorithms and techniques developed for area minimization in pipelined datapath [21] power minimization under timing constraint [19, 20], etc. In [21] the design entry is a pipelined datapath. In the problem formulation, there are a set of constraints regarding the number of registers and depth of pipeline stages, which are not considered in budgeting on directed acyclic graphs. All the proposed algorithms are heuristic ....

J. Luo and N. Jha. "Battery-Aware Static Scheduling for Distributed Real-Time Embedded Systems". In the proceedings of IEEE/ACM Design Automation Conference, 2001.

....in the area of high level synthesis where timing slack of the nodes in the data flow graphs are considered for better optimization in area and power. Examples are the algorithms and techniques developed for area minimization in pipelined datapath [21] power minimization under timing constraint [19, 20], etc. In [21] the design entry is a pipelined datapath. In the problem formulation, there are a set of constraints regarding the number of registers and depth of pipeline stages, which are not considered in budgeting on directed acyclic graphs. All the proposed algorithms are heuristic ....

J. Luo and N. Jha. "Battery-Aware Static Scheduling for Distributed Real-Time Embedded Systems". In the proceedings of IEEE/ACM Design Automation Conference, 2001.

.... performance and battery life in mobile systems [23] They proposed a few case studies on a real portable embedded system with non ideal batteries and a variable voltage processor [24, 25, 26] Luo and Jha proposed an instruction scheduling technique based on certain battery discharge patterns [22]. The idea is to smooth the power profile curve as a way to maximize battery life. Benini et al. proposed a discrete time model for batteries as a way to estimate battery life. They noticed that if the battery can be disconnected from the load after a certain period of discharge, it can regain ....

J. Luo and N. K. Jha. Battery-aware static scheduling for distributed real-time embedded systems. In Proc. Design Automation Conference, pages 444--449, June 2001.

....minimal energy while satisfying all constraints. Fig. 2 summarizes the energy costs. Another problem not highlighted with this example is that mode changesmay incur nontrivial power or timing overhead. If so, overhead must be considered in determining the feasibility of the mode schedule. In [8, 9], Luo and Jha present static scheduling for multiple processing elements (PEs) They re order tasks and apply voltage scaling in this post processing step after scheduling to smooth the system level power profile. Their tasks have precedenceand timing constraints, though power is a design goal ....

....that has resource dependency. a) Initial schedule and power profile; b) greedy voltage clock scaling results in a power spike that violate maximum power constraint; c) a feasible solution meets both power and timing constraints, and saves energy as well. DPM LPS MS [11, 12] 13, 14] 5,3,6,7] [8, 9] Timing as constraint N N Y Y Y Power as constraint N N N N Y Timing overhead Y Y N N Y Power overhead Y Y N N Y Multiple resources N Y N Y Y Figure 3: Comparison of dynamic power management (DPM) low power scheduling (LPS) and mode selection (MS) 3 Modeling Resource Dependency ....

J. Luo and N. K. Jha. Battery-aware static scheduling for distributed real-time embedded systems. In Proceedings of Design Automation Conference, pages 444--449, 2001.

....by exploiting the processors voltage scaling capabilities. Related techniques that optimize for power consumption of processors typically assume a fixed communication data rate. 4] uses simulated heating search strategies to find low power design points for voltage scalable embedded processors. [11] performs battery aware task post scheduling for distributed, voltage scalable processors by moving tasks to smooth the power profile. 16, 15] propose partitioning the computation onto a multi processor architecture that consumes significantly less power than a single processor. 6] reduces ....

J. Luo and N. K. Jha. Battery-aware static scheduling for distributed real-time embedded systems. In Proc. Design Automation Conference, pages 444--449, June 2001.

....the results are not generalizable to multiple processors. What these DVS techniques have in common is that they are greedy and assume a single processor. A power aware embedded system, however, consists of multiple resources, which may be one or more processors and peripheral devices. Luo and Jha [26, 28] presents static scheduling for multiple processing elements (PEs) by reordering tasks and applying voltage scaling in this post processing step to smooth the system level power profile. Unfortunately, all of these greedy DVS techniques fail to generalize to multiple resources when there are ....

....we increase the dynamic range of power by increasing parallelism. Second, they have not considered inter component dependency in a system, with the exception of Qiu, Qu and Pedram in [35] which models multiple service providers and their Generalized DPM DVS MS [45, 16] 3, 34] 13, 38, 39, 36] [26, 28] Timing as constraint N N Y Y Y Power as constraint N N N N Y Timing overhead Y Y N N Y Power overhead Y Y N N Y Multiple resources N Y N Y Y Figure 3: Comparison of dynamic power management (DPM) dynamic voltage scaling (DVS) and mode selection (MS) Stochastic Petri Net (GSPN) can ....

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J. Luo and N. K. Jha. Battery-aware static scheduling for distributed real-time embedded systems. In Proc. Design Automation Conference, pages 444--449, June 2001.

....The most important issues to be considered here are the total battery capacity, expressed in Ampere hours or Watt hours and the battery discharge profile. The latter is important in devising battery aware schemes that are guided by the discharge profile. In one such recent work, Luo and Jha [54] consider distributed real time systems and develop a battery model, which is used in two scheduling schemes: first they optimize the battery discharge power profile, and then they use voltage scaling for distributed real time systems. The overall objective is to extend the battery lifespan while ....

....which reduces the average discharge current level. In the case of systems with more than one means of power delivery, a different metric is needed. In their approach, Liu et al. 55] study the Mars rover application. They develop a maximum power budget concept and propose heuristics similar to [54] for a dual power source system. A power usage metric (free vs. expensive) is developed from a depletion point of view: free solar power cell and an expensive battery based power source. FPGA for Real Time: FPGA based systems are considered to be less energy efficient compared to other ....

J. Luo, N. K. Jha, "Battery-Aware Static Scheduling for Distributed Real-Time Embedded Systems", 38th ACM/IEEE Design Automation Conference, DAC'01, 2001, pp. 444-449.

....of the communication interfaces power consumption. Related techniques that optimize for power consumption of processors typically assume a fixed communication data rate. 3] uses simulated heating search strategies to find low power design points for voltage scalable embedded processors. [9] performs batteryaware task post scheduling for distributed, voltage scalable processors by moving tasks to smooth the power profile. 12, 11] propose partitioning the computation onto a multi processor architecture that consumes significantly less power than a single processor. 4] reduces ....

J. Luo and N. K. Jha. Battery-aware static scheduling for distributed real-time embedded systems. In Proc. Design Automation Conference, pages 444--449, June 2001.

....the processors voltage scaling capabilities. Related techniques that optimize for power apply voltage scaling to the processors while assuming a fixed communication data rate. 3] uses simulated heating search strategies to find low power design points for voltage scalable embedded processors. [7] performs battery aware task post scheduling for distributed, voltage scalable processors by moving tasks to smooth the power profile. 10] proposes a multi processor architecture that consumes significantly less power than a single processor on the same task by partitioning an image processing ....

J. Luo and N. K. Jha. Battery-aware static scheduling for distributed real-time embedded systems. In Proc. Design Automation Conference, pages 444--449, June 2001.

....single device schemes. In task scheduling, researchers have paid most attention to a single processor with voltage clock scaling capability while power modes and power consumption on peripheral devices are not considered. The cost of mode changes on the processor is often reasonably neglected [6, 9, 10]. In dynamic power management techniques, researchers concentrated on systems of a single device without strong timing guarantees [2, 7, 11] Tradeoffs are made between the power consumption and system performance. In [8] the authors did model multiple servers and relationships in the system. ....

J. Luo and N. K. Jha. Battery-aware static scheduling for distributed real-time embedded systems. In Proc. of DAC, pages 444--449, 2001.

....include battery driven speed and voltage setting [12, 13] These approaches are typically aimed at general purpose computing systems, and hence do not exploit characteristics of the application. Battery driven voltage setting and task scheduling for application specific systems are described in [7, 14]. While the latter can be applied only to systems for which a static schedule exists, neither approach lends itself to dynamic control over the system power profile. Extensions to conventional dynamic power management techniques have been suggested in [15] to take battery constraints into ....

J. Luo and N. K. Jha, "Battery-aware static scheduling for distributed real-time embedded systems," in Proc. Design Automation Conf., pp. 444--449, June 2001.

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J. Luo and N. Jha, ""Battery-Aware Static Scheduling for Distributed Real-Time Embedded Systems"," in IEEE/ACM Design Automation Conference, 2001.

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J. Luo and N. Jha. "Battery-Aware Static Scheduling for Distributed Real-Time Embedded Systems". In IEEE/ACM Design Automation Conference, pages 444--449, 2001.

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J. Luo and N. K. Jha, "Battery-Aware Static Scheduling for Distributed Real-Time Embedded Systems," in Design Automation Conference, 2001, pp. 444--449.

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J. Luo and N. K. Jha, "Battery-Aware Static Scheduling for Distributed Real-Time Embedded Systems," in Design Automation Conference, 2001, pp. 444--449.

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J. Luo, N. K. Jha, "Battery-aware static scheduling for distributed real-time embedded systems," in Proceedings of the 38th Conference on Design Automation, pp. 444-449, 2001.

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J. Luo and N. K. Jha. Battery-Aware Static Scheduling for Distributed Real-time Embedded Systems. In proceedings of 38th Design Automation Conference, 2001. Page(s): 444 -449.

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J. Luo and N. K. Jha. Battery-Aware Static Scheduling for Distributed Real-Time Embedded Systems. In Proc. DAC01, 2001.

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J. Luo and N. K. Jha. "Battery-Aware Static Scheduling for Distributed RealTime Embedded Systems", 38th ACM/IEEE Design Automation Conference, pp. 444-449, June 2001.

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J. Luo and N. K. Jha. Battery-Aware Static Scheduling for Distributed Real-Time Embedded Systems. In Proc. DAC01, 2001.

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J. Luo and N. K. Jha, "Battery-aware static scheduling for distributed real-time embedded systems," in Proc. Design Automation Conf., June 2001, pp. 444--449.

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J. Luo and N. K. Jha. Battery-aware static scheduling for distributed real-time embedded systems. In Proc. Design Automation Conference, pages 444--449, June 2001.

No context found.

J. Luo and N. K. Jha. Battery-aware static scheduling for distributed real-time embedded systems. In Proceedings of Design Automation Conference, pages 444--449, 2001.

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