• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.6B
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• pp.641-647
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• 2009

This paper proposes an improved predictive dynamic power management (DPM) scheme and a task scheduling algorithm to reduce unnecessary power consumption in embedded systems. The proposed algorithm performs pre-scheduling to minimize unnecessary power consumption. The proposed predictive DPM utilizes a scheduling library provided by the system to reduce computation overhead. Experimental results show that the proposed algorithm can reduce power consumption by 22.3% on the average comparing with the LLF algorithm for DPM-enable system scheduling.

• Proceedings of the IEEK Conference
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• 2003.07b
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• pp.1153-1156
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• 2003

In this paper, we present a reducing power consumption of a scheduling for module selection under the time constraint. Traditional high-level synthesis do not allow reuse of complex, realistic datapath component during the task of scheduling. On the other hand, the proposed scheduling of reducing power consumption is able to approach a productivity of the design the low power to reuse which given a library of user-defined datapath component and to share of resource sharing on the switching activity in a shared resource. Also, we are obtainable the optimal the scheduling result in experimental results of our approach various HLS benchmark environment using chaining and multi-cycling in the scheduling techniques..

• Proceedings of the IEEK Conference
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• 2004.06b
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• pp.459-462
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• 2004

In this paper, we present a reducing power consumption of a scheduling for module selection under the time constraint. A a reducing power consumption of a scheduling for module selection under the time constraint execute scheduling and allocation for considering the switching activity. The focus scheduling of this phase adopt Force-Directed Scheduling for low power to existed Force-Directed Scheduling. and it constructs the module selection RT library by in account consideration the mutual correlation of parameters in which the power and the area and delay. when it is, in this paper we formulate the module selection method as a multi-objective optimization and propose a branch and bound approach to explore the large design space of module selection. Therefore, the optimal module selection method proposed to consider power, area, delay parameter at the same time. The comparison experiment analyzed a point of difference between the existed FDS algorithm and a new FDS_RPC algorithm.

• Journal of Electrical Engineering and Technology
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• v.9 no.6
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• pp.1843-1850
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• 2014

In this paper, we investigate a group building based power consumption scheduling to minimize the electricity cost. We consider the demand shift to reduce the peak load and suggest the compensation function reflecting the relationship between the change of the building demand and the occupants' comfort. Using that, the electricity cost minimization problem satisfied the convexity is formulated, and the optimal power consumption scheduling algorithm is proposed based on the iterative method. Extensive simulations show that the proposed algorithm achieves the group management gain compared to the individual building operation by increasing the degree of freedom for the operation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.11C
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• pp.1132-1138
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• 2002

This paper proposes a reducing power consumption of a scheduling algorithm for optimal selection of supply voltage. In scheduling of reduction power consumption, we determine the control steps of operations to be executed by exploiting the possibility of using variable voltage levels to reduce power consumption. In the optimal selection of supply voltage binding, we minimize the main factor of the power consumption of the switching activity on the registers using a graph coloring technique. From a set of experiments using high-level benchmark examples, we show that the proposed algorithm prefer to use optimal selection supply voltages rather than uniformed single voltage is effective in reducing power consumption.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.3A
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• pp.318-323
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• 2004

In this paper, we present a reducing power consumption of a scheduling for reuse module selection under the time constraint. Traditional high-level synthesis do not allow reuse of complex, realistic datapath component during the task of scheduling. On the other hand, the proposed scheduling of reducing power consumption is able to approach a productivity of the design the low power to reuse which given a library of user-defined datapath component and to share of resource sharing on the switching activity in a shared resource. Also, we are obtainable the optimal the scheduling result in experimental results of our approach various HLS benchmark environment using chaining and multi-cycling in the scheduling techniques.

• The KIPS Transactions:PartA
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• v.18A no.3
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• pp.93-98
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• 2011

While the use of battery-powered embedded systems has been rapidly increasing, the current level of battery technology has not kept up with the drastic increase in power consumption by the system. In order to prolong system usage time, the battery size needs to be increased. The amounts of power consumption by embedded systems are determined by their hardware configuration and software for manipulating hardware resources. In spite of that, the hardware provides features for lowering power consumption, if those cannot be utilized efficiently by software including operating system, reduction in power consumption is not optimized. In this paper, we propose a BET(Break Even Time)-based scheduling method using task partition to reduce power consumption of multimedia applications in a mobile embedded system environment.

• Journal of Information Processing Systems
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• v.5 no.4
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• pp.175-186
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• 2009

Power consumed by modern computer systems, particularly servers in data centers has almost reached an unacceptable level. However, their energy consumption is often not justifiable when their utilization is considered; that is, they tend to consume more energy than needed for their computing related jobs. Task scheduling in distributed computing systems (DCSs) can play a crucial role in increasing utilization; this will lead to the reduction in energy consumption. In this paper, we address the problem of scheduling precedence-constrained parallel applications in DCSs, and present two energy- conscious scheduling algorithms. Our scheduling algorithms adopt dynamic voltage and frequency scaling (DVFS) to minimize energy consumption. DVFS, as an efficient power management technology, has been increasingly integrated into many recent commodity processors. DVFS enables these processors to operate with different voltage supply levels at the expense of sacrificing clock frequencies. In the context of scheduling, this multiple voltage facility implies that there is a trade-off between the quality of schedules and energy consumption. Our algorithms effectively balance these two performance goals using a novel objective function and its variant, which take into account both goals; this claim is verified by the results obtained from our extensive comparative evaluation study.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.9
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• pp.2213-2221
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• 2015

The ondemand governor used in android-based smartphone platforms is a CPU frequency scaling technique. The ondemand governor sets the CPU operating frequency depending on the CPU utilization rate. Job scheduling affects the CPU utilization rate. The power consumption is proportional to the value of operating frequency. Consequently, CPU frequency scaling and CPU utilization rate have an effect on power consumption in a smartphone. In this paper, we evaluated the performance of job scheduling techniques incorporating the ondemand governor in terms of CPU utilization, power consumption, and job deadline miss ratio.

• Journal of the Korea Society of Computer and Information
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• v.19 no.4
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• pp.25-33
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• 2014

This paper proposes a power-efficient scheduling scheme that stochastically minimizes the power consumption of real-time tasks while meeting their deadlines on multicore processors. In the proposed scheme, uncertain computation amounts of given tasks are translated into probabilistic computation amounts based on their past completion amounts, and the mean power consumption of the translated probabilistic computation amounts is minimized with a finite set of discrete clock frequencies. Also, when system load is low, the proposed scheme activates a part of all available cores with unused cores powered off, considering the leakage power consumption of cores. Evaluation shows that the scheme saves up to 69% power consumption of the previous method.