• Title/Summary/Keyword: 명령어 연관성

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Variable Input Gshare Predictor based on Interrelationship Analysis of Instructions (명령어 연관성 분석을 통한 가변 입력 gshare 예측기)

  • Kwak, Jong-Wook
    • Journal of the Korea Society of Computer and Information
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    • v.13 no.4
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    • pp.19-30
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    • 2008
  • Branch history is one of major input vectors in branch prediction. Therefore, the Proper use of branch history plays a critical role of improving branch prediction accuracy. To improve branch prediction accuracy, this paper proposes a new branch history management policy, based on interrelationship analysis of instructions. First of all, we propose three different algorithms to analyze the relationship: register-writhing method, branch-reading method, and merged method. Then we additionally propose variable input gshare predictor as an implementation of these algorithms. In simulation part, we provide performance differences among the algorithms and analyze their characteristics. In addition, we compare branch prediction accuracy between our proposals and conventional fixed input predictors. The performance comparison for optimal input branch predictor is also provided.

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Performance Evaluation of Secure Embedded Processor using FEC-Based Instruction-Level Correlation Technique (오류정정 부호 기반 명령어 연관성 기법을 적용한 임베디드 보안 프로세서의 성능평가)

  • Lee, Seung-Wook;Kwon, Soon-Gyu;Kim, Jong-Tae
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.34 no.5B
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    • pp.526-531
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    • 2009
  • In this paper, we propose new novel technique (ILCT: Instruction-Level Correlation Technique) which can detect tempered instructions by software attacks or hardware attacks before their execution. In conventional works, due to both high complex computation of cipher process and low processing speed of cipher modules, existing secure processor architecture applying cipher technique can cause serious performance degradation. While, the secure processor architecture applying ILCT with FEC does not incur excessive performance decrease by complexity of computation and speed of tampering detection modules. According to experimental results, total memory overhead including parity are increased in average of 26.62%. Also, secure programs incur CPI degradation in average of $1.20%{\sim}1.97%$.

The Instruction Flash memory system with the high performance dual buffer system (명령어 플래시 메모리를 위한 고성능 이중 버퍼 시스템 설계)

  • Jung, Bo-Sung;Lee, Jung-Hoon
    • Journal of the Korea Society of Computer and Information
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    • v.16 no.2
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    • pp.1-8
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    • 2011
  • NAND type Flash memory has performing much researches for a hard disk substitution due to its low power consumption, cheap prices and a large storage. Especially, the NAND type flash memory is using general buffer systems of a cache memory for improving overall system performance, but this has shown a tendency to emphasize in terms of data. So, our research is to design a high performance instruction NAND type flash memory structure by using a buffer system. The proposed buffer system in a NAND flash memory consists of two parts, i.e., a fully associative temporal buffer for branch instruction and a fully associative spatial buffer for spatial locality. The spatial buffer with a large fetching size turns out to be effective serial instructions, and the temporal buffer with a small fetching size can achieve effective branch instructions. According to the simulation results, we can reduce average miss ratios by around 77% and the average memory access time can achieve a similar performance compared with the 2-way, victim and fully associative buffer with two or four sizes.

Performance Improvement of Speech Recognition Using Context and Usage Pattern Information (문맥 및 사용 패턴 정보를 이용한 음성인식의 성능 개선)

  • Song, Won-Moon;Kim, Myung-Won
    • The KIPS Transactions:PartB
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    • v.13B no.5 s.108
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    • pp.553-560
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    • 2006
  • Speech recognition has recently been investigated to produce more reliable recognition results in a noisy environment, by integrating diverse sources of information into the result derivation-level or producing new results through post-processing the prior recognition results. In this paper we propose a method which uses the user's usage patterns and the context information in speech command recognition for personal mobile devices to improve the recognition accuracy in a noisy environment. Sequential usage (or speech) patterns prior to the current command spoken are used to adjust the base recognition results. For the context information, we use the relevance between the current function of the device in use and the spoken command. Our experiment results show that the proposed method achieves about 50% of error correction rate over the base recognition system. It demonstrates the feasibility of the proposed method.

Instruction Queue Architecture for Low Power Microprocessors (마이크로프로세서 전력소모 절감을 위한 명령어 큐 구조)

  • Choi, Min;Maeng, Seung-Ryoul
    • Journal of the Institute of Electronics Engineers of Korea SD
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    • v.45 no.11
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    • pp.56-62
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    • 2008
  • Modern microprocessors must deliver high application performance, while the design process should not subordinate power. In terms of performance and power tradeoff, the instructions window is particularly important. This is because a large instruction window leads to achieve high performance. However, naive scaling conventional instruction window can severely affect the complexity and power consumption. This paper explores an architecture level approach to reduce power dissipation. We propose a low power issue logic with an efficient tag translation. The direct lookup table (DTL) issue logic eliminates the associative wake-up of conventional instruction window. The tag translation scheme deals with data dependencies and resource conflicts by using bit-vector based structure. Experimental results show that, for SPEC2000 benchmarks, the proposed design reduces power consumption by 24.45% on average over conventional approach.

The Strategic Thinking of Mathematically Gifted Elementary Students in LOGO Project Learning (LOGO를 이용한 프로젝트 학습에서 나타난 초등 수학영재 학생들의 전략적 사고)

  • Lew, Hee-Chan;Jang, In-Ok
    • Journal of Educational Research in Mathematics
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    • v.20 no.4
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    • pp.459-476
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    • 2010
  • The purpose of this study is to suggest a new direction in using LOGO as a gifted education program and to seek an effective approach for LOGO teaching and learning, by analyzing the strategic thinking of mathematically gifted elementary students. This research is exploratory and inquisitive qualitative inquiry, involving observations and analyses of the LOGO Project learning process. Four elementary students were selected and over 12 periods utilizing LOGO programming, data were collected, including screen captures from real learning situations, audio recordings, observation data from lessons involving experiments, and interviews with students. The findings from this research are as follows: First, in LOGO Project Learning, the mathematically gifted elementary students were found to utilize such strategic ways of thinking as inferential thinking in use of prior knowledge and thinking procedures, generalization in use of variables, integrated thinking in use of the integration of various commands, critical thinking involving evaluation of prior commands for problem-solving, progressive thinking involving understanding, and applying the current situation with new viewpoints, and flexible thinking involving the devising of various problem solving skills. Second, the students' debugging in LOGO programming included comparing and constrasting grammatical information of commands, graphic and procedures according to programming types and students' abilities, analytical thinking by breaking down procedures, geometry-analysis reasoning involving analyzing diagrams with errors, visualizing diagrams drawn following procedures, and the empirical reasoning on the relationships between the whole and specifics. In conclusion, the LOGO Project Learning was found to be a program for gifted students set apart from other programs, and an effective way to promote gifted students' higher-level thinking abilities.

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A Study on Anomaly Detection based on User's Command Analysis (사용자 명령어 분석을 통한 비정상 행위 판정에 관한 연구)

  • 윤정혁;오상현;이원석
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.10 no.4
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    • pp.59-71
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    • 2000
  • Due to the advance of computer and communication technology, intrusions or crimes using a computer have been increased rapidly while various information has been provided to users conveniently. As a results, many studies are necessary to detect the activities of intruders effectively. In this paper, a new association algorithm for the anomaly detection model is proposed in the process of generating user\`s normal patterns. It is that more recently observed behavior get more affection on the process of data mining. In addition, by clustering generated normal patterns for each use or a group of similar users, it is possible to identify the usual frequency of programs or command usage for each user or a group of uses. The performance of the proposed anomaly detection system has been tested on various system Parameters in order to identify their practical ranges for maximizing its detection rate.

A Development of Curriculum for Information Security Professional Manpower Training (정보보안 전문인력 양성을 위한 교육과정 개발)

  • Lee, Moongoo
    • Journal of the Institute of Electronics and Information Engineers
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    • v.54 no.1
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    • pp.46-52
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    • 2017
  • Social attention to information security field is inspired, and manpower demand forecast of this area is getting high. This study surveyed information security knowledge of practitioners who work in a field of information security such as computer and network system. We analyzed a connection between survey data, information protection job system that was suggested by NICE, IT skills that NCS and KISA classified and security field classification system. Base on data that analyzed, this study suggests a curriculum that trains professional manpower who perform duties in the field of information security. Suggested curriculum can be applied to 2 year college, 3 year college and 4 year college. Suggested curriculum provides courses that students who want to work in a field of information security must learn during the college. Suggested courses are closely connected to a related field and detailed guideline is indicated to each course to educate. Suggested curriculum is required, and it combines a theoretical education that become basis and a practical education so that it is not weighted to learn theory and is not only focusing on learning simple commands. This curriculum is established to educate students countermeasures of hacking and security defend that based on scenario that connected to executive ability. This curriculum helps to achieve certificates related to a field more than paper qualification. Also, we expect this curriculum helps to train convergent information security manpower for next generation.

An Approach of Scalable SHIF Ontology Reasoning using Spark Framework (Spark 프레임워크를 적용한 대용량 SHIF 온톨로지 추론 기법)

  • Kim, Je-Min;Park, Young-Tack
    • Journal of KIISE
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    • v.42 no.10
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    • pp.1195-1206
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    • 2015
  • For the management of a knowledge system, systems that automatically infer and manage scalable knowledge are required. Most of these systems use ontologies in order to exchange knowledge between machines and infer new knowledge. Therefore, approaches are needed that infer new knowledge for scalable ontology. In this paper, we propose an approach to perform rule based reasoning for scalable SHIF ontologies in a spark framework which works similarly to MapReduce in distributed memories on a cluster. For performing efficient reasoning in distributed memories, we focus on three areas. First, we define a data structure for splitting scalable ontology triples into small sets according to each reasoning rule and loading these triple sets in distributed memories. Second, a rule execution order and iteration conditions based on dependencies and correlations among the SHIF rules are defined. Finally, we explain the operations that are adapted to execute the rules, and these operations are based on reasoning algorithms. In order to evaluate the suggested methods in this paper, we perform an experiment with WebPie, which is a representative ontology reasoner based on a cluster using the LUBM set, which is formal data used to evaluate ontology inference and search speed. Consequently, the proposed approach shows that the throughput is improved by 28,400% (157k/sec) from WebPie(553/sec) with LUBM.