• JSTS:Journal of Semiconductor Technology and Science
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• 제15권6호
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• pp.678-684
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• 2015

Near-threshold computing (NTC) is now regarded as a promising candidate for innovative power reduction, which cannot be achieved with conventional super-threshold computing (STC). However, performance degradation and vulnerability to process variation in the NTC regime are the primary concerns. In this paper, we propose a voltage- and frequency-tuning methodology for mitigating the process-variation-induced problems in NTC-based manycore architectures. To implement the proposed methodology, we build up multiple-voltage multiple-frequency (MVMF) islands and apply a voltage-frequency tuning algorithm based on the critical-path monitoring technique to reduce the effects of process variation and maximize energy efficiency in the post-silicon stage. Experimental results show that the proposed methodology reduces overall power consumption by 8.2-20.0%, compared to existing methods in variation-sensitive NTC environments.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2015년도 추계학술대회
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• pp.866-869
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• 2015

시스템-온-칩(system-on-chip, SoC)내에 집적되는 소자의 수가 기하급수적으로 증가함에 따라 에너지 효율을 높이기 위한 전압 스케일링은 필수적인 요소가 되었다. 문턱전압 근처 동작(near-threshold voltage computing, NTC)은 칩 에너지 효율을 10배 가까이 향상시킬 수 있는 기술로서 전통적인 초 문턱전압 동작(super-threshold voltage computing, STC)의 한계를 극복할 수 있을 것으로 기대되고 있다. 저성능 매니코어(manycore) 시스템으로 동작하는 NTC는 에너지 효율을 극대화할 수 있지만 성능 유지를 위한 코어 수의 증가는 상당한 면적 증가를 수반한다. 본 논문에서는 성능, 전력 및 면적 간의 trade-off를 고려하여 면적 제약조건 하에서 NTC 코어 수 및 캐시 및 클러스터 크기 결정 알고리즘을 통해 요구 성능을 만족시키면서 전력 소모를 최적화하는 방법을 제안한다. 실험을 통해 면적 제약조건 속에서 기존의 STC 코어에서의 성능을 유지한 채 전력소모를 약 16.5% 감소시킬 수 있음을 보여준다.

• 한국정보통신학회논문지
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• 제4권5호
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• pp.971-976
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• 2000

The main objective of this paper is to present the rain cell size distribution observed during squall line episodes in the Sudano-Sahelian region. The used data were collected during the EPSAT Program [Etude des Precipitation par SATellite (Satellites Study of Precipitation)] which has been developed since 1958, on an experimental area located near Niamey, Niger (2 10′32"E, 13 28′38"N). The data were obtained with a C-band radar and a network composed of approximately 100 raingages over a 10,000 $\textrm{km}^2$. In this work a culling of the squall line episodes was made for the 1992 rainy season. After radar data calibration using the raingage network a number of PPI (Plan Position Indicator) images were generated. Each image was then treated in order to obtain a series of radar reflectivity (Z) maps. To describe the cell distribution, a contouring program was used to analyze the areas with rain rate greater than or equal to the contour threshold (R$\geq$$\tau$). 24700 contours were generated, where each iso-pleth belongs to a predefined threshold. Computing each cell surface and relating its area to an equi-circle (a circle having the same area as the cell), a statistical analysis was made. The results show that the number of rain cells having a given size is an inverse exponential function of the equivalent radius. The average and median equivalent radii ate 1.4 and 0.69 In respectively. Implications of these results for the precipitation estimation using threshold methods are discussed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권10호
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• pp.3378-3393
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• 2014

We have witnessed the rapid development of information technology in recent years. One of the key phenomena is the fast, near-exponential increase of data. Consequently, most of the traditional data classification methods fail to meet the dynamic and real-time demands of today's data processing and analyzing needs--especially for continuous data streams. This paper proposes an improved incremental learning algorithm for a large-scale data stream, which is based on SVM (Support Vector Machine) and is named DS-IILS. The DS-IILS takes the load condition of the entire system and the node performance into consideration to improve efficiency. The threshold of the distance to the optimal separating hyperplane is given in the DS-IILS algorithm. The samples of the history sample set and the incremental sample set that are within the scope of the threshold are all reserved. These reserved samples are treated as the training sample set. To design a more accurate classifier, the effects of the data volumes of the history sample set and the incremental sample set are handled by weighted processing. Finally, the algorithm is implemented in a cloud computing system and is applied to study user behaviors. The results of the experiment are provided and compared with other incremental learning algorithms. The results show that the DS-IILS can improve training efficiency and guarantee relatively high classification accuracy at the same time, which is consistent with the theoretical analysis.

• International Journal of Computer Science & Network Security
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• 제21권11호
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• pp.345-353
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• 2021

Cloud computing represent a new era of computing that's forms through the combination of service-oriented architecture (SOA), Internet and grid computing with virtualization technology. Virtualization is a concept through which every cloud is enable to provide on-demand services to the users. Most IT service provider adopt cloud based services for their users to meet the high demand of computation, as it is most flexible, reliable and scalable technology. Energy based performance tradeoff become the main challenge in cloud computing, as its acceptance and popularity increases day by day. Cloud data centers required a huge amount of power supply to the virtualization of servers for maintain on- demand high computing. High power demand increase the energy cost of service providers as well as it also harm the environment through the emission of CO₂. An optimization of cloud computing based on energy-performance tradeoff is required to obtain the balance between energy saving and QoS (quality of services) policies of cloud. A study about power usage of resources in cloud data centers based on workload assign to them, says that an idle server consume near about 50% of its peak utilization power [1]. Therefore, more number of underutilized servers in any cloud data center is responsible to reduce the energy performance tradeoff. To handle this issue, a lots of research proposed as energy efficient algorithms for minimize the consumption of energy and also maintain the SLA (service level agreement) at a satisfactory level. VM (virtual machine) consolidation is one such technique that ensured about the balance of energy based SLA. In the scope of this paper, we explore reinforcement with fuzzy logic (RFL) for VM consolidation to achieve energy based SLA. In this proposed RFL based active VM consolidation, the primary objective is to manage physical server (PS) nodes in order to avoid over-utilized and under-utilized, and to optimize the placement of VMs. A dynamic threshold (based on RFL) is proposed for over-utilized PS detection. For over-utilized PS, a VM selection policy based on fuzzy logic is proposed, which selects VM for migration to maintain the balance of SLA. Additionally, it incorporate VM placement policy through categorization of non-overutilized servers as- balanced, under-utilized and critical. CloudSim toolkit is used to simulate the proposed work on real-world work load traces of CoMon Project define by PlanetLab. Simulation results shows that the proposed policies is most energy efficient compared to others in terms of reduction in both electricity usage and SLA violation.

• 한국정보과학회논문지:정보통신
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• 제27권2호
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• pp.138-148
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• 2000

이동 컴퓨팅 환경에서 이동 호스트(클라이언트)는 네트워크가 연결되어 있는 동안 단절에 대비하여 중요한 파일들을 자신의 로컬 캐쉬에 저장하여야 한다. 본 논문에서는 클라이언트가 네트워크 단절시 가까운 미래에 사용하게 될 파일을 캐쉬에 저장하는 선인출 메커니즘을 제안한다. 이 메커니즘은 분석기, 선인출 목록 생성기, 그리고 선인출 관리기를 활용한다. 분석기는 클라이언트의 파일 참조 기록을 FAP(File Access Pattern) 프로파일에 저장한다. 선인출 목록 생성기는 이 프로파일을 이용하여 선인출 목록을 만들며, 선인출 관리기는 이 선인출 목록을 파일 서버에게 요청한다. 본 논문은 단지 관련성이 깊은 파일들이 선인출되는 것을 보장하기 위해 TRP(Threshold of Reference Probability) 파라미터를 설정하였다. 선인출 목록 생성기는 참조 확률이 TRP 이상인 파일들을 선인출 목록에 추가한다. 또한, 본 논문은 선인출 목록을 저장하는데 필요한 적재 크기를 줄이기 위해 TACP(Threshold of Access Counter Probability) 파라미터를 사용한다. 마지막으로, 우리는 캐쉬 적중률, 단절 후 클라이언트의 참조 파일 수, 적재 크기를 측정하였다. 시뮬레이션 결과, 선인출 메커니즘의 성능이 LRU 캐슁 메커니즘 보다 우수함을 알 수 있었다. 또한, TACP를 이용한 선인출은 적재 크기를 줄일 수 있으면서도, TACP를 사용하지 않는 선인출과 바슷한 성능을 보임을 확인하였다.

• 대한임베디드공학회논문지
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• 제18권4호
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• pp.159-164
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• 2023

Solar energy harvesting IoT devices prioritize maximizing the utilization of collected energy due to the periodic recharging nature of solar energy, rather than minimizing energy consumption. Meanwhile, research on edge AI, which performs machine learning near the data source instead of the cloud, is actively conducted for reasons such as data confidentiality and privacy, response time, and cost. One such research area involves performing various audio AI applications using audio data collected from multiple IoT devices in an IoT edge computing environment. However, in most studies, IoT devices only perform sensing data transmission to the edge server, and all processes, including data preprocessing, are performed on the edge server. In this case, it not only leads to overload issues on the edge server but also causes network congestion by transmitting unnecessary data for learning. On the other way, if data preprocessing is delegated to each IoT device to address this issue, it leads to another problem of increased blackout time due to energy shortages in the devices. In this paper, we aim to alleviate the problem of increased blackout time in devices while mitigating issues in server-centric edge AI environments by determining where the data preprocessed based on the energy state of each IoT device. In the proposed method, IoT devices only perform the preprocessing process, which includes sound discrimination and noise removal, and transmit to the server if there is more energy available than the energy threshold required for the basic operation of the device.

• International Journal of Contents
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• 제18권2호
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• pp.68-80
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• 2022

The health of the human heart is commonly measured using ECG (Electrocardiography) signals. To identify any anomaly in the human heart, the time-sequence of ECG signals is examined manually by a cardiologist or cardiac electrophysiologist. Lightweight anomaly detection on ECG signals in an embedded system is expected to be popular in the near future, because of the increasing number of heart disease symptoms. Some previous research uses deep learning networks such as LSTM and BiLSTM to detect anomaly signals without any handcrafted feature. Unfortunately, lightweight LSTMs show low precision and heavy LSTMs require heavy computing powers and volumes of labeled dataset for symptom classification. This paper proposes an ECG anomaly detection system based on two level BiLSTM for acceptable precision with lightweight networks, which is lightweight and usable at home. Also, this paper presents a new threshold technique which considers statistics of the current ECG pattern. This paper's proposed model with BiLSTM detects ECG signal anomaly in 0.467 ~ 1.0 F₁ score, compared to 0.426 ~ 0.978 F₁ score of the similar model with LSTM except one highly noisy dataset.

• 대한원격탐사학회지
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• 제32권5호
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• pp.471-482
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• 2016

본 논문에서는 수계 영역의 감독 분류 성능을 향상시키기 위하여 블록 기반의 영상 분할과 수계 경계의 확장을 이용하는 수계 검출 방법을 제안한다. 초기 수계 영역을 추출하기 위하여 수계 훈련 지역의 Normalized Difference Water Index (NDWI) 및 Near Infrared (NIR) 밴드 영상의 분광 정보를 이용하여 Mahalanobis 거리 영상을 생성한다. Mahalanobis 거리 영상에 포함된 잡음 성분의 영향을 감소시키기 위해서 인접한 화소의 연결 강도에 의해 확산 계수가 제어되는 평균 곡률 확산을 적용한 후에 초기 수계 영역을 추출한다. 추출된 수계 영상을 같은 크기의 블록으로 분할한 후에 수계 경계에 속하는 수계 영역의 정보를 이용하여 수계 영역을 갱신한다. 수계 경계에 속하는 수계 영역과 수계 훈련 지역 사이의 통계적인 거리가 임계값 이하이면, 수계 영역 갱신을 반복적으로 수행한다. 제안한 알고리즘을 KOMPSAT-2 영상에 적용한 결과 블록 크기가 $11{\times}11$에서 $19{\times}19$사이인 경우에 overall accuracy는 99.47%에서 99.53%, Kappa coefficient는 95.07%에서 95.80%의 분류 정확도를 보였다.