• Title/Summary/Keyword: Word Level Multiplier

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Word Level Multiplier for $GF(2^m)$ Using Gaussian Normal Basis (가우시안 정규기저를 이용한 $GF(2^m)$상의 워드-레벨 곱셈기)

  • Kim, Chang-Hoon;Kwon, Yun-Ki;Kim, Tae-Ho;Kwon, Soon-Hak;Hong, Chun-Pyo
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.31 no.11C
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    • pp.1120-1127
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    • 2006
  • [ $GF(2^m)$ ] for elliptic curve cryptosystem. The proposed multiplier uses Gaussian normal basis representation and produces multiplication results at a rate of one per [m/w] clock cycles, where w is the selected we.4 size. We implement the p.oposed design using Xilinx XC2V1000 FPGA device. Our design has significantly less critical path delay compared with previously proposed hard ware implementations.

The Study of Developing Korean SentiWordNet for Big Data Analytics : Focusing on Anger Emotion (빅데이터 분석을 위한 한국어 SentiWordNet 개발 방안 연구 : 분노 감정을 중심으로)

  • Choi, Sukjae;Kwon, Ohbyung
    • The Journal of Society for e-Business Studies
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    • v.19 no.4
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    • pp.1-19
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    • 2014
  • Efforts to identify user's recognition which exists in the big data are being conducted actively. They try to measure scores of people's view about products, movies and social issues by analyzing statements raised on Internet bulletin boards or SNS. So this study deals with the problem of determining how to find the emotional vocabulary and the degree of these values. The survey methods are using the results of previous studies for the basic emotional vocabulary and degree, and inferring from the dictionary's glosses for the extended emotional vocabulary. The results were found to have the 4 emotional words lists (vocabularies) as basic emotional list, extended 1 stratum 1 level list from basic vocabulary's glosses, extended 2 stratum 1 level list from glosses of non-emotional words, and extended 2 stratum 2 level list from glosses' glosses. And we obtained the emotional degrees by applying the weight of the sentences and the emphasis multiplier values on the basis of basic emotional list. Experimental results have been identified as AND and OR sentence having a weight of average degree of included words. And MULTIPLY sentence having 1.2 to 1.5 weight depending on the type of adverb. It is also assumed that NOT sentence having a certain degree by reducing and reversing the original word's emotional degree. It is also considered that emphasis multiplier values have 2 for 1 stratum and 3 for 2 stratum.

Design of a Bit-Level Super-Systolic Array (비트 수준 슈퍼 시스톨릭 어레이의 설계)

  • Lee Jae-Jin;Song Gi-Yong
    • Journal of the Institute of Electronics Engineers of Korea SD
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    • v.42 no.12
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    • pp.45-52
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    • 2005
  • A systolic array formed by interconnecting a set of identical data-processing cells in a uniform manner is a combination of an algorithm and a circuit that implements it, and is closely related conceptually to arithmetic pipeline. High-performance computation on a large array of cells has been an important feature of systolic array. To achieve even higher degree of concurrency, it is desirable to make cells of systolic array themselves systolic array as well. The structure of systolic array with its cells consisting of another systolic array is to be called super-systolic array. This paper proposes a scalable bit-level super-systolic amy which can be adopted in the VLSI design including regular interconnection and functional primitives that are typical for a systolic architecture. This architecture is focused on highly regular computational structures that avoids the need for a large number of global interconnection required in general VLSI implementation. A bit-level super-systolic FIR filter is selected as an example of bit-level super-systolic array. The derived bit-level super-systolic FIR filter has been modeled and simulated in RT level using VHDL, then synthesized using Synopsys Design Compiler based on Hynix $0.35{\mu}m$ cell library. Compared conventional word-level systolic array, the newly proposed bit-level super-systolic arrays are efficient when it comes to area and throughput.