• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.924-927
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• 2003

In this paper, we design the memory tester chip set playing an important role in the memory tester as central parts. Memory tester has the sixteen inner instructions to control the test sequence and the address and data signals to DUT. These instructions are saved in memory with each chip such as sequence chip and address/data generator chip. Sequence chip controls the test sequence according to instructions saved in the memory. And Generator chip generates the address and data signals according to instructions saved in the memory, too.

• International journal of advanced smart convergence
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• v.6 no.1
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• pp.26-31
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• 2017

When mobile navigator device accesses data randomly, the cache memory performance is rapidly deteriorated due to low memory access locality. For instance, GPS (General Positioning System) of navigator program for automobiles or drones, that are currently in common use, uses data from 32 satellites and computes current position of a receiver. This computation of positioning is the major part of GPS which accounts more than 50% computation in the program. In this computation task, the satellite signals are received in real time and stored in buffer memories. At this task, since necessary data cannot be sequentially stored, the data is read and used at random. This data accessing patterns are generated randomly, thus, memory system performance is worse by low data locality. As a result, it is difficult to process data in real time due to low data localization. Improving the low memory access locality inherited on the algorithms of conventional communication applications requires a certain optimization technique to solve this problem. In this study, we try to do optimizations with data and memory to improve the locality problem. In experiment, we show that our case study can improve processing speed of core computation and improve our overall system performance by 14%.

• Journal of the Korean Data and Information Science Society
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• v.26 no.5
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• pp.1035-1045
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• 2015

Recent studies in Big Data Analysis are showing promising results, utilizing the main memory for rapid data processing. In-memory computing technology can be highly advantageous when used with high-performing servers having tens of gigabytes of RAM with multi-core processors. The constraint in network in these infrastructure can be lessen by combining in-memory technology with distributed parallel processing. This paper discusses the research in the aforementioned concept applying to a test taxi hailing application without disregard to its underlying RDBMS structure. The application of IMDG technology in the application's backend API without restructuring the database schema yields 6 to 9 times increase in performance in data processing and throughput. Specifically, the change in throughput is very small even with increase in data load processing.

• The Journal of the Korea Contents Association
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• v.8 no.4
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• pp.48-54
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• 2008

This paper analyzes cost tradeoffs between memory usage and computation for window-based operators in data stream environments. It identifies generic operator network constructs, and sets up a cost model for the estimation of the expected memory reduction and the computation overheads when window memory relocations are applied to each operator network construct. This cost model helps to identify the utility of window memory relocations. It also helps to apply window memory relocation to improve a query execution plan to save memory usage. The proposed approach contributes to expand the scope of query processing and optimization in data stream environments. It also provides a basis to develop a cost estimation model for the query optimization using window memory relocations.

• JSTS:Journal of Semiconductor Technology and Science
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• v.14 no.4
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• pp.391-406
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• 2014

The performance of General-Purpose computation on Graphics Processing Units (GPGPU) is heavily dependent on the memory access behavior. This sensitivity is due to a combination of the underlying Massively Parallel Processing (MPP) execution model present on GPUs and the lack of architectural support to handle irregular memory access patterns. Application performance can be significantly improved by applying memory-access-pattern-aware optimizations that can exploit knowledge of the characteristics of each access pattern. In this paper, we present an algorithmic methodology to semi-automatically find the best mapping of memory accesses present in serial loop nest to underlying data-parallel architectures based on a comprehensive static memory access pattern analysis. To that end we present a simple, yet powerful, mathematical model that captures all memory access pattern information present in serial data-parallel loop nests. We then show how this model is used in practice to select the most appropriate memory space for data and to search for an appropriate thread mapping and work group size from a large design space. To evaluate the effectiveness of our methodology, we report on execution speedup using selected benchmark kernels that cover a wide range of memory access patterns commonly found in GPGPU workloads. Our experimental results are reported using the industry standard heterogeneous programming language, OpenCL, targeting the NVIDIA GT200 architecture.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.1
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• pp.77-82
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• 2014

SD memory cards are widely used in portable digital devices, and most of them exploit NAND flash memory as their storage, so that they have a feature of storing users' important data safely with low costs. In case of using NAND flash memory as storage, however, there is no method to store users' data if memory capacity is insufficient when transferring a large volume of data. This paper proposes a virtual SD memory card system. It used a SD memory card device driver to process data requested from a host by exploiting external storage rather than by exploiting flash memory as a memory core for storing data to the SD memory card. For experiment, it used the FPGA-based SD card slave controller IP on the SMC controller with a S3C2450 ARM CPU to test.

• Journal of Korea Spatial Information System Society
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• v.11 no.2
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• pp.79-88
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• 2009

The volume of memory to store real-time data stream is varied dynamically. Continuous queries processing the data stream must manage the storage volume dynamically. In previous research, according to current volume of data a general memory manager which allocates and releases memory by a page unit is researched.However, the method frequently executes page allocation and release to store data stream. Moreover, particularly delayed queries can monopolize many of pages because the method directly allocates pages when a query has not enough memory. Focusing on the problems in memory management systems, this research proposes a memory management method which reduces the frequency of allocation and release and uniformly distributes pages for queries. The method can reduce the frequency of allocation and release through allocation based on utilization ratio of pages in each query and prevent memory monopoly through memory allocation which considers query delay.

• The Journal of the Korea Contents Association
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• v.12 no.1
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• pp.30-37
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• 2012

A memory system with the linear skewing scheme has been regarded as one of suitable memory systems for a single instruction, multiple data (SIMD) architecture. The memory system supports simultaneous access n data to m memory modules within various access types with a constant interval in an arbitrary position in two dimensional data array of $M{\times}N$. Although $m{\times}cells$ memory cells are physically required to support logical two dimensional $M{\times}N$ array of data by means of the memory system, at least (m-n)${\times}cells$ memory cells remain in disuse, where cells is (M-1)/q+(N-1)/$p{\times}{\lceil}M/q{\rceil}+1$. On keeping functionalities the memory system supports, $(n{\times}t){\times}N/p$ out of a number of unused memory cells, where t>0, being used as local buffer memories for n processing elements is proposed in this paper.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.2
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• pp.87-96
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• 2019

Low power issue is one of the most critical problems in the Internet of Things (IoT), which are powered by battery. To solve this problem, various approaches have been presented so far. In this paper, we propose a method to reduce the power consumption by reducing the numbers of accesses into the flash memory consuming a large amount of power for on-chip software execution. Our approach is based on using cooperative logging structure to distribute the sampling overhead in single sensor node to adjacent nodes in case of rare-event applications. The proposed algorithm to identify event occurrence is newly introduced with negative feedback method by observing difference between past data and recent data coming from the sensor. When an event with need of flash access is determined, the proposed approach only allows access to write the sampled data in flash memory. The proposed event detection algorithm (EDA) result in 30% reduction of power consumption compared to the conventional flash write scheme for all cases of event. The sampled data from the sensor is first traced into the random access memory (RAM), and write access to the flash memory is delayed until the page buffer of the on-chip flash memory controller in the micro controller unit (MCU) is full of the numbers of the traced data, thereby reducing the frequency of accessing flash memory. This technique additionally reduces power consumption by 40% compared to flash-write all data. By sharing the sampling information via LoRa channel, the overhead in sampling data is distributed, to reduce the sampling load on each node, so that the 66% reduction of total power consumption is achieved in several IoT edge nodes by removing the sampling operation of duplicated data.

• IEMEK Journal of Embedded Systems and Applications
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• v.12 no.3
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• pp.177-184
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• 2017

High performance non-volatile memory system can mitigate the gap between main memory and storage. However, no single memory devices fulfill the requirements. Non-volatile Dual In-line Memory Module (NVDIMM) consisted of DRAMs and NAND Flashes has been proposed to achieve the performance and non-volatility simultaneously. When power outage occurs, data in DRAM is backed up into NAND Flash using a small-size external energy storage such as a supercapacitor. Backup and restore operations of NVDIMM do not cooperate with the operating system in the NVDIMM standard, thus there is room to optimize its operation. This paper analysis the operation of NVDIMM and proposes a method to reduce backup and restore time. Particularly, data compression is introduced to reduce the amount of data that to be backed up and restored. The simulation results show that the proposed method reduces up to 72.6% of backup and restore time.