• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.71-72
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• 2013

최근 연구 개발되고 있는 스토리지 클래스 메모리는 정체되어 있는 스토리지와 DRAM 산업에 큰 변화를 가져올 것으로 예상된다. 현재 컴퓨팅 환경에서 스토리지의 성능 저하요소가 큰 이슈로 야기되어지는 가운데 본 논문에서는 TPC-C 벤치마크를 이용하여 임의 쓰기와 덮어 쓰기 연산 시 발생되는 문제점들을 분석한다. 실험 결과를 통해 향후 스토리지 클래스 메모리를 활용하여 기존 쓰기 연산 시 발행 하는 문제점들을 해결할 수 있는 방안에 대해 논의 한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.235-236
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• 2009

비휘발성 나노 저장 소자는 고속의 바이트 단위 접근성과 함께 비휘발성을 동시에 갖고 있다. 이와 같은 특징은 차세대 장치로 주목 받을 만큼 오늘날의 컴퓨터 구조에 큰 변화를 줄 수 있는 잠재력을 갖고 있으며 이를 접목한 시스템적인 개발 역시 활발하게 진행되고 있다. 본 논문에서는 기존의 메인 메모리와 나노 저장 소자가 융합된 스토리지 메모리 클래스(SCM) 환경 하에서 입출력시에 원자성(Atomicity)이 보장되도록 설계, 구현된 새도우 블록 기법을 소개하고, 더불어 캐시를 사용하며 발생할 수 있는 데이터 일관성 처리의 보장을 다루었다. 또한 실제 FRAM이 장착된 하드웨어 환경 하에서 개선된 새도우 블록을 동작하여 측정한 성능 결과를 함께 제공한다.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.5
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• pp.35-40
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• 2010

NAND flash memory has been widely used than traditional HDD in PDA and other mobile devices, embedded systems, PC because of faster access speed, low power consumption, vibration resistance and other benefits. DiskSim and other HDD simulators has been developed that for find improvements for the software or hardware. But there is a few Linux-based simulators for NAND flash memory and SSD. There is necessary for Windows-based NAND flash simulator because storage devices and PC using Windows. This paper describe for development of simulator-NFSim for FTL performance analysis in NAND flash. NFSim is used to measure performance of various FTL algorithms and FTL wear-level. NAND flash memory model and FTL algorithm developed using Windows Driver Model and class for scalability. There is no need for another tools because NFSim using graph tool for data measure of FTL performance.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.2
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• pp.53-60
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• 2012

Nearest-neighbor classification predicts the class of an input data with the most frequent class among the near training data of the input data. Even though nearest-neighbor classification doesn't have a training stage, all of the training data are necessary in a predictive stage and the generalization performance depends on the quality of training data. Therefore, as the training data size increase, a nearest-neighbor classification requires the large amount of memory and the large computation time in prediction. In this paper, we propose a prototype selection algorithm that predicts the class of test data with the new set of prototypes which are near-boundary training data. Based on Tomek links and distance metric, the proposed algorithm selects boundary data and decides whether the selected data is added to the set of prototypes by considering classes and distance relationships. In the experiments, the number of prototypes is much smaller than the size of original training data and we takes advantages of storage reduction and fast prediction in a nearest-neighbor classification.

• Journal of Korea Multimedia Society
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• v.23 no.8
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• pp.1019-1029
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• 2020

In this paper, we propose a novel knowledge distillation algorithm to create an compressed deep ensemble network coupled with the combined use of local and global features of face images. In order to transfer the capability of high-level recognition performances of the ensemble deep networks to a single deep network, the probability for class prediction, which is the softmax output of the ensemble network, is used as soft target for training a single deep network. By applying the knowledge distillation algorithm, the local feature informations obtained by training the deep ensemble network using facial subregions of the face image as input are transmitted to a single deep network to create a so-called compressed ensemble DCNN. The experimental results demonstrate that our proposed compressed ensemble deep network can maintain the recognition performance of the complex ensemble deep networks and is superior to the recognition performance of a single deep network. In addition, our proposed method can significantly reduce the storage(memory) space and execution time, compared to the conventional ensemble deep networks developed for face recognition.

• Journal of the Korean institute of surface engineering
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• v.53 no.6
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• pp.330-342
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• 2020

Since the original report on ferroelectricity in Si-doped HfO2 in 2011, fluorite-structured ferroelectrics have attracted increasing interest due to their scalability, established deposition techniques including atomic layer deposition, and compatibility with the complementary-metal-oxide-semiconductor technology. Especially, the emerging fluorite-structured ferroelectrics are considered promising for the next-generation semiconductor devices such as storage class memories, memory-logic hybrid devices, and neuromorphic computing devices. For achieving the practical semiconductor devices, understanding polarization switching kinetics in fluorite-structured ferroelectrics is an urgent task. To understand the polarization switching kinetics and domain dynamics in this emerging ferroelectric materials, various classical models such as Kolmogorov-Avrami-Ishibashi model, nucleation limited switching model, inhomogeneous field mechanism model, and Du-Chen model have been applied to the fluorite-structured ferroelectrics. However, the polarization switching kinetics of fluorite-structured ferroelectrics are reported to be strongly affected by various nonideal factors such as nanoscale polymorphism, strong effect of defects such as oxygen vacancies and residual impurities, and polycrystallinity with a weak texture. Moreover, some important parameters for polarization switching kinetics and domain dynamics including activation field, domain wall velocity, and switching time distribution have been reported quantitatively different from conventional ferroelectrics such as perovskite-structured ferroelectrics. In this focused review, therefore, the polarization switching kinetics of fluorite-structured ferroelectrics are comprehensively reviewed based on the available literature.