• The Journal of the Korea Contents Association
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• v.12 no.7
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• pp.11-20
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• 2012

This paper presents new instruction-level power model for an asynchronous processor, A8051. Even though the proposed model estimates power consumption as instruction level, this model reflects the behavioral features of asynchronous pipeline during the program is executed. Thus, it can effectively enhance the accuracy of power model for an asynchronous embedded processor without significant complexity of power model as well as the increase of simulation time. The proposed power model is based on the implementation of A8051 to reflect the characteristics of power consumption in A8051. The simulation results of the proposed model is compared with that of gate-level synthesized A8051. The proposed power model shows the accuracy of 94% and the simulation time for estimation the power consumption was reduced to 1,600 times.

• Electronics and Telecommunications Trends
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• v.12 no.5 s.47
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• pp.95-106
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• 1997

집적회로 설계에서 소비 전력은 집적도가 증가함에 따라서 중요한 설계 사양으로 전력 소비를 낮추기 위한 저전력 설계 기술에 대한 연구가 많이 진행되고 있다. 저전력 설계 기술은 소비 전력에 대한 정확한 예측 기술과 예측된 결과를 이용한 최적화 기술로 나뉘어 진다. 이들 기술은 논리 수준에서 많은 연구가 진행되었으며 현재, 효과적인 예측과 최적화가 가능한 행위 및 아키텍처 수준의 상위 수준에서 저전력 설계에 대한 연구가 진행되고 있다. 저전력 설계를 위한 최적화 기술, CAD 환경, 그리고 툴에 대하여 살펴보고 상위수준합성 시스템인 HYPER에 대하여 간략하게 소개한다

• Journal of IKEEE
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• v.23 no.4
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• pp.1373-1380
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• 2019

In order to increase energy efficiency, developments of the advanced metering infrastructure (AMI) in the smart grid technology have recently been actively conducted. An essential part of AMI is analyzing power consumption and forecasting consumption patterns. In this paper, we analyze the power consumption and summarized the data errors. Monthly power consumption patterns are also analyzed using the k-means clustering algorithm. Forecasting the consumption pattern by each household is difficult. Therefore, we first classify the data into 100 clusters and then predict the average of the next day as the daily average of the clusters based on the deep neural network. Using practically collected AMI data, we analyzed the data errors and could successfully conducted power forecasting based on a clustering technique.

• Journal of Internet Computing and Services
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• v.17 no.1
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• pp.7-14
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• 2016

Recently, as cloud computing technologies are developed, it enable to work anytime and anywhere by smart phone and computer. Also, cloud computing technologies are suited to reduce costs of maintaining IT infrastructure and initial investment, so cloud computing has been developed. As demand about cloud computing has risen sharply, problems of power consumption are occurred to maintain the environment of data center. To solve the problem, first of all, power consumption has been measured. Although using power meter to measure power consumption obtain accurate power consumption, extra cost is incurred. Thus, we propose prediction method about power consumption without power meter. To proving accuracy about proposed method, we perform CPU and Hard disk test on cloud computing environment. During the tests, we obtain both predictive value by proposed method and actual value by power meter, and we calculate error rate. As a result, error rate of predictive value and actual value shows about 4.22% in CPU test and about 8.51% in Hard disk test.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.7
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• pp.3152-3159
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• 2012

This paper presents the new instruction-level power model for an asynchronous processor. Until now, the various power models for estimating the power dissipation of embedded processor in SoC are proposed. Since all of them are target to the synchronous processors, the accuracy is questionable when we apply those power models to the asynchronous processor in SoC. To solve this problem, we present new power model for an asynchronous processor by reflecting the behavioral features of an asynchronous circuit. The proposed power model is verified using an implementation of asynchronous processor, A8051. The simulation results of the proposed model is compared with the measurement result of gate-level synthesized A8051. The proposed power model shows the accuracy of 90.7% and the simulation time for estimation the power consumption was reduced to 1,900 times.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11a
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• pp.877-879
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• 2005

배터리를 이용하는 모바일 장비에서 전력 소비를 줄이기 위한 많은 연구들이 있다. 그 중에 동적 전력 관리(Dynamic Power Management)는 시스템의 각 컴포넌트의 상태를 쉽게 관찰할 수 있다는 측면에서 운영체제에서 접근하기 적합한 전력 관리 방법이다. 본 논문에서는 대표적인 모바일 장비인 노트북에서 하드 디스크의 전력소비를 줄이기 위하여 기계 학습 기반의 동적 전력 관리 방법을 제안한다. 하드 디스크 접근 패턴을 분석하여 Artificial Neural Network(ANN) 기법으로 모형을 만들고 이 모형을 바탕으로 하드 디스크의 다음 유휴기간을 예측하였다. 예측된 유휴기간 동안 하드 디스크로의 공급 전력을 감소시키지 않았을 경우에 소비하는 비용이 전력을 줄였다 다시 늘이는 비용보다 크다면 하드 디스크로 공급되는 전력을 줄임으로써 유휴기간 동안 낭비되는 배터리 전력을 줄일 수 있었다. 본 연구에서 생성된 모형을 하드 디스크 디바이스 드라이버에 적용하면 기존의 시간 경계 값을 이용한 방법에 비해 약 23.05W의 전력 소비 감소를 기대할 수 있다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.8
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• pp.1422-1427
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• 2006

In order to estimate the power consumed by an embedded microprocessor during an execution of software, we measure and utilize the current consumed by the processor during the execution of each instruction. In this paper, we measure and analyse the current consumed by the microprocessor adc16s310 during the execution of arithmetic and logic instructions, and propose a power estimation model which estimates the current for all instruction executions precisely by using a small numbers of current measurements. The proposed model can estimate the current with an average 0.34% error by using only 5.84% of total current measurements for arithmetic and logic instructions of the processor.

• Smart Media Journal
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• v.12 no.11
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• pp.18-26
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• 2023

The building energy management system (BEMS), a system designed to efficiently manage energy production and consumption, aims to address the variable nature of power consumption within buildings due to their physical characteristics, necessitating stable power supply. In this context, accurate prediction of building energy consumption becomes crucial for ensuring reliable power delivery. Recent research has explored various approaches, including time series analysis, statistical analysis, and artificial intelligence, to predict power consumption. This paper analyzes the strengths and weaknesses of the Prophet model, choosing to utilize its advantages such as growth, seasonality, and holiday patterns, while also addressing its limitations related to data complexity and external variables like climatic data. To overcome these challenges, the paper proposes an algorithm that combines the Prophet model's strengths with the gated recurrent unit (GRU) to forecast short-term (2 days) and medium-term (7 days, 15 days, 30 days) building energy consumption. Experimental results demonstrate the superior performance of the proposed approach compared to conventional GRU and Prophet models.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.8
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• pp.1569-1580
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• 2016

For the 2.4 GHz active RFID to be successful in the market, one of the requirements is the increased battery life. However, currently we do not have any accurate power consumption estimation method. In this study we develop a simulation model, which can be used to estimate power consumption of tag accurately. Six different simulation models are proposed depending on collision algorithm and query command method. To improve estimation accuracy, we classify tag operating modes as the wake-up receive, UHF receive, sleep timer, tag response, and sleep modes. Power consumption and operating time are identified according to the tag operating mode. Query command for simplifying collection and ack command procedure and newly developed collision control algorithm are used in the simulation. Other performance measures such as throughput, recognition time for multi-tags, tag recognition rate including power consumption are compared with those from the current standard ISO/IEC 18000-7.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.1
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• pp.17-25
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• 2023

As the range of use of urban unmanned aerial vehicles (UAV) expands, it is necessary to operate UAVs efficiently because of its limited battery capacity. For this, it is required to find the optimal flight profile with various simulations. Therefore, it is important to predict the power and energy consumption of the UAV battery. In this paper, we analyzed the relationship between the speed and acceleration of the UAV and power consumption during the flight. Then, we derived a linear model, which is easily utilized. In addition, we also derived an accurate power consumption model based on deep neural network learning. To find the efficient model, we used learning data as 1) the GPS 3-axis velocity and acceleration data, 2) the IMU 3-axis velocity only, and 3) the IMU 3-axis velocity and acceleration data. The final model shows 5.86% error rate for power consumption and 1.50% error rate for the cumulative energy consumption.