• Electronics and Telecommunications Trends
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• v.28 no.1
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• pp.33-41
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• 2013

64bit 범용 서버의 활용 확산, 메모리 가격의 하락 등 하드웨어의 발전과 실시간성을 요구하는 응응 분야의 확대로 인해 인-메모리 컴퓨팅 기술에 대한 관심이 높아지고 있다. 인-메모리 컴퓨팅 기술은 응용 서비스의 클라우드화, 모바일화, 글로벌화로 인해 발생하는 익스트림 트랜잭션의 고성능 처리를 지원하기 위한 기반 기술로 활용이 확대되고 있다. 또한 빅데이터를 효과적으로 활용하기 위해서 빅데이터라는 원석을 보석으로 가공하는 데 있어서 실시간성을 제공하기 위한 기반 플랫폼으로서 활용이 시도되고 있다. 본고에서는 고성능 트랜잭션 처리를 필요로 하는 통신, 금융 등 특정 분야에서 주로 활용되던 인-메모리 DBMS(Datbase Management System) 기술이 익스트림 트랜잭션 서비스 환경, 빅데이터 실시간 분석 환경 등 새로운 서비스 환경을 지원하기 위한 기술 발전 동향에 대해 조사한다.

• Journal of Platform Technology
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• v.11 no.4
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• pp.71-79
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• 2023

In this paper, we analyze the characteristics of machine learning workloads and, based on them, propose a distributed in-memory caching technique to improve the performance of machine learning workloads. The core of machine learning workload is model training, and model training is a computationally intensive task. Performing machine learning workloads in a Kubernetes-based cloud environment in which the computing framework and storage are separated can effectively allocate resources, but delays can occur because IO must be performed through network communication. In this paper, we propose a distributed in-memory caching technique to improve the performance of machine learning workloads performed in such an environment. In particular, we propose a new method of precaching data required for machine learning workloads into the distributed in-memory cache by considering Kubflow pipelines, a Kubernetes-based machine learning pipeline management tool.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.2
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• pp.291-298
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• 2023

SRAM-based in-memory computing is one of the technologies to solve the bottleneck of von Neumann architecture. In order to achieve SRAM-based in-memory computing, it is essential to design efficient SRAM bit-cell. In this paper, we propose a low-power differential sensing 8+T SRAM bit-cell which reduces power consumption and improves circuit performance. The proposed 8+T SRAM bit-cell is applied to ripple carry adder which performs SRAM read and bitwise operations simultaneously and executes each logic operation in parallel. Compared to the previous work, the designed 8+T SRAM-based ripple carry adder is reduced power consumption by 11.53%, but increased propagation delay time by 6.36%. Also, this adder is reduced power-delay-product (PDP) by 5.90% and increased energy-delay- product (EDP) by 0.08%. The proposed circuit was designed using TSMC 65nm CMOS process, and its feasibility was verified through SPECTRE simulation.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.777-784
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• 2023

A sense amplifier is an essential peripheral circuit for designing a memory and is used to sense a small differential input signal and amplify it into digital signal. In this paper, a high-speed sense amplifier applicable to in-memory computing circuits is proposed. The proposed circuit reduces sense delay time through transistor Mtail that provides an additional discharge path and improves the circuit performance of the sense amplifier by applying m-GDI (: modified Gate Diffusion Input). Compared with previous structure, the sense delay time was reduced by 16.82%, the PDP(: Power Delay Product) by 17.23%, the EDP(: Energy Delay Product) by 31.1%. The proposed circuit was implemented using TSMC's 65nm CMOS process, while its feasibility was verified through SPECTRE simulation in this study.

• Korea Information Processing Society Review
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• v.19 no.2
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• pp.45-53
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• 2012

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.3
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• pp.26-33
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• 2017

Recently container technologies have been receiving attention for efficient use of the cloud platform. Container virtualization technology has the advantage of a highly portable, high density when compared with the existing hypervisor. Container virtualization technology, however, uses a virtualization technology at the operating system level, which is shared by a single kernel to run multiple instances. For this reason, the feature of container is that the attacker can obtain the root privilege of the host operating system internal the container. Due to the characteristics of the container, the attacker can attack the root privilege of the host operating system in the container utilizing the vulnerability of the kernel. In this paper, we propose a framework for efficiently detecting and responding to root privilege attacks of a host operating system in a container. This framework uses a memory trap technique to detect changes in a specific memory area of a container and to suspend the operation of the container when it is detected.

• KIISE Transactions on Computing Practices
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• v.23 no.5
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• pp.293-298
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• 2017

Recently, much research has been devoted to implementing and provisioning high-performance computing environment using clusters with multiple computers and high-performance networking technologies. In-memory based Key-Value stores, such as Redis or Memcached, are widely used in high performance cluster environments to improve the data processing performance. We can distribute data at different storage nodes, and each computing node can access it at a high speed using these In-memory based Key-Value stores. InfiniBand is a de-facto technology that is widely used to interconnect each node of a cluster. In this paper, we propose a new data pre-fetching scheme for Key-Value store based on high performance clusters to improve the performance. The proposed scheme utilizes the data transfer characteristics of InfiniBand. The results of the simulation show that the proposed scheme can reduce the data transfer time by up to about 28%.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.4
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• pp.775-783
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• 2017

As interest in cloud computing grows, the number of users using cloud computing services is increasing. However, cloud computing technology has been steadily challenged by security concerns. Therefore, various security breaches are springing up to enhance the system security for cloud services users. In particular, research on detection of malicious VM (Virtual Machine) is actively underway through the introspecting virtual machines on the cloud platform. However, memory analysis technology is not used as a monitoring tool in the environments where multiple virtual machines are run on a single server platform due to obstructive monitoring overhead. As a remedy to the challenging issue, we proposes a computationally efficient instance memory introspection scheme to minimize the overhead that occurs in memory dump and monitor it through a partial memory monitoring based on the well-defined kernel memory map library.

• Journal of Korean Society for Geospatial Information Science
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• v.25 no.1
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• pp.29-36
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• 2017

In this paper, we implement a spatial big data analysis prototype based on Spark which is an in-memory system and compares the performance by the spatial split algorithm on this basis. In cluster computing environments, big data is divided into blocks of a certain size order to balance the computing load of big data. Existing research showed that in the case of the Hadoop based spatial big data system, the split method by spatial is more effective than the general sequential split method. Hadoop based spatial data system stores raw data as it is in spatial-divided blocks. However, in the proposed Spark-based spatial analysis system, there is a difference that spatial data is converted into a memory data structure and stored in a spatial block for search efficiency. Therefore, in this paper, we propose an in-memory spatial big data prototype and a spatial split block storage method. Also, we compare the performance of existing spatial split algorithms in the proposed prototype. We presented an appropriate spatial split strategy with the Spark based big data system. In the experiment, we compared the query execution time of the spatial split algorithm, and confirmed that the BSP algorithm shows the best performance.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.5
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• pp.125-133
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• 2016

Data processing capacity and speed are rapidly increasing due to the development of hardware and software in recent time. As a result, data usage is geometrically increasing and the amount of data which computers have to process has already exceeded five-thousand transaction per second. That is, the importance of Big Data is due to its 'real-time' and this makes it possible to analyze all the data in order to obtain accurate data at right time under any circumstances. Moreover, there are many researches about this as construction of smart factory with the application of Big Data is expected to have reduction in development, production, and quality management cost. In this paper, system using In-Memory Data Grid for high speed processing is implemented in semiconductor process which numerous data occur and improved performance is proven with experiments. Implemented system is expected to be possible to apply on not only the semiconductor but also any fields using Big Data and further researches will be made for possible application on other fields.