• 정보처리학회논문지A
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• 제19A권1호
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• pp.51-60
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• 2012

일반적으로 스마트카드라고 불리는 IC(Integrated Circuit)카드는 작은 크기의 마이크로칩(MPU)과 메모리, EEPROM, 카드 운영체제(COS) 및 보안 알고리즘을 내장하고 있다. 이러한 IC카드는 금융(카드,은행,증권 등), 교통, 통신, 의료, 전자여권, 멥버쉽 회원관리 등 거의 모든 산업분야에서 이용되고 있다. 최근 방송통신융합 및 TV의 스마트기기화 추세에 따라 TV전자상거래(T-커머스)가 방송산업의 신성장 동력이 되면서 T-커머스 지불결제 방법으로 IC카드를 이용하는 등 응용분야가 증가하고 있다. 예를 들어, T-커머스에서 IC신용카드(또는 IC현금카드)를 이용하여 결제를 하거나, IC현금카드를 이용하여 ATM과 같은 방식으로 TV뱅킹 서비스를 제공한다. 하지만 아직까지 대부분의 T-커머스 신용카드 결제 서비스는 리모콘을 이용한 카드정보 입력 방식을 이용하고 있기 때문에 고객 편의성이 크게 떨어지고, 카드정보 저장 및 노출 등 보안성에 있어서 취약성을 가지고 있다. 이러한 문제점을 해결하고자, 본 논문에서는 IC신용카드 결제 표준기술인 EMV기술을 이용한 TV전자 지불 결제시스템 구현을 위한 결제처리 모듈을 개발하였다.

• 한국컴퓨터정보학회논문지
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• 제17권6호
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• pp.1-11
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• 2012

공정기술이 지속적으로 발달함에 따라 멀티코어 프로세서는 성능 향상이라는 장점과 함께 내부 연결망의 긴 지연 시간, 높은 전력 소모, 그리고 발열 현상 등의 문제점들을 내포하고 있다. 이와 같은 2차원 멀티코어 프로세서의 문제점들을 해결하기 위한 방안 중 하나로 3차원 멀티코어 프로세서 구조가 주목을 받고 있다. 3차원 멀티코어 프로세서는 TSV를 이용하여 수직으로 쌓은 여러 개의 레이어들을 연결함으로써 2차원 멀티코어 프로세서와 비교하여 배선 길이를 크게 줄일 수 있다. 하지만, 3차원 멀티코어 프로세서에서는 여러 개의 코어들이 수직으로 적층되므로 전력밀도가 증가하고, 이로 인해 발열문제가 발생하여 높은 냉각 비용과 함께 신뢰성에 부정적인 영향을 유발한다. 따라서 3차원 멀티코어 프로세서를 설계할 때에는 성능과 함께 온도를 반드시 고려하여야 한다. 본 논문에서는 캐쉬 구성에 따른 3차원 쿼드코어 프로세서의 온도를 상세히 분석하고, 이를 기반으로 발열문제를 해결하기 위해저온도 캐쉬 구성 방식을 제안하고자 한다. 실험결과, 명령어 캐쉬는 최고온도가 임계값보다 낮고 데이터 캐쉬는 많은 웨이를 가지는 구성을 적용할 때 최고온도가 임계값보다 높아짐을 알 수 있다. 또한, 본 논문에서 제안하는 캐쉬구성은 쿼드코어 프로세서를 사용하는 3차원 구조에서 캐쉬의 온도 감소에 효과적일 뿐만 아니라 성능 저하 또한 거의 없음을 알 수 있다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.975-976
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• 1993

This talk presents the overview of the author's research and development activities on fuzzy inference hardware. We involved it with two distinct approaches. The first approach is to use application specific integrated circuits (ASIC) technology. The fuzzy inference method is directly implemented in silicon. The second approach, which is in its preliminary stage, is to use more conventional microprocessor architecture. Here, we use a quantitative technique used by designer of reduced instruction set computer (RISC) to modify an architecture of a microprocessor. In the ASIC approach, we implemented the most widely used fuzzy inference mechanism directly on silicon. The mechanism is beaded on a max-min compositional rule of inference, and Mandami's method of fuzzy implication. The two VLSI fuzzy inference chips are designed, fabricated, and fully tested. Both used a full-custom CMOS technology. The second and more claborate chip was designed at the University of North Carolina(U C) in cooperation with MCNC. Both VLSI chips had muliple datapaths for rule digital fuzzy inference chips had multiple datapaths for rule evaluation, and they executed multiple fuzzy if-then rules in parallel. The AT & T chip is the first digital fuzzy inference chip in the world. It ran with a 20 MHz clock cycle and achieved an approximately 80.000 Fuzzy Logical inferences Per Second (FLIPS). It stored and executed 16 fuzzy if-then rules. Since it was designed as a proof of concept prototype chip, it had minimal amount of peripheral logic for system integration. UNC/MCNC chip consists of 688,131 transistors of which 476,160 are used for RAM memory. It ran with a 10 MHz clock cycle. The chip has a 3-staged pipeline and initiates a computation of new inference every 64 cycle. This chip achieved an approximately 160,000 FLIPS. The new architecture have the following important improvements from the AT & T chip: Programmable rule set memory (RAM). On-chip fuzzification operation by a table lookup method. On-chip defuzzification operation by a centroid method. Reconfigurable architecture for processing two rule formats. RAM/datapath redundancy for higher yield It can store and execute 51 if-then rule of the following format: IF A and B and C and D Then Do E, and Then Do F. With this format, the chip takes four inputs and produces two outputs. By software reconfiguration, it can store and execute 102 if-then rules of the following simpler format using the same datapath: IF A and B Then Do E. With this format the chip takes two inputs and produces one outputs. We have built two VME-bus board systems based on this chip for Oak Ridge National Laboratory (ORNL). The board is now installed in a robot at ORNL. Researchers uses this board for experiment in autonomous robot navigation. The Fuzzy Logic system board places the Fuzzy chip into a VMEbus environment. High level C language functions hide the operational details of the board from the applications programme . The programmer treats rule memories and fuzzification function memories as local structures passed as parameters to the C functions. ASIC fuzzy inference hardware is extremely fast, but they are limited in generality. Many aspects of the design are limited or fixed. We have proposed to designing a are limited or fixed. We have proposed to designing a fuzzy information processor as an application specific processor using a quantitative approach. The quantitative approach was developed by RISC designers. In effect, we are interested in evaluating the effectiveness of a specialized RISC processor for fuzzy information processing. As the first step, we measured the possible speed-up of a fuzzy inference program based on if-then rules by an introduction of specialized instructions, i.e., min and max instructions. The minimum and maximum operations are heavily used in fuzzy logic applications as fuzzy intersection and union. We performed measurements using a MIPS R3000 as a base micropro essor. The initial result is encouraging. We can achieve as high as a 2.5 increase in inference speed if the R3000 had min and max instructions. Also, they are useful for speeding up other fuzzy operations such as bounded product and bounded sum. The embedded processor's main task is to control some device or process. It usually runs a single or a embedded processer to create an embedded processor for fuzzy control is very effective. Table I shows the measured speed of the inference by a MIPS R3000 microprocessor, a fictitious MIPS R3000 microprocessor with min and max instructions, and a UNC/MCNC ASIC fuzzy inference chip. The software that used on microprocessors is a simulator of the ASIC chip. The first row is the computation time in seconds of 6000 inferences using 51 rules where each fuzzy set is represented by an array of 64 elements. The second row is the time required to perform a single inference. The last row is the fuzzy logical inferences per second (FLIPS) measured for ach device. There is a large gap in run time between the ASIC and software approaches even if we resort to a specialized fuzzy microprocessor. As for design time and cost, these two approaches represent two extremes. An ASIC approach is extremely expensive. It is, therefore, an important research topic to design a specialized computing architecture for fuzzy applications that falls between these two extremes both in run time and design time/cost. TABLEI INFERENCE TIME BY 51 RULES {{{{Time }}{{MIPS R3000 }}{{ASIC }}{{Regular }}{{With min/mix }}{{6000 inference 1 inference FLIPS }}{{125s 20.8ms 48 }}{{49s 8.2ms 122 }}{{0.0038s 6.4㎲ 156,250 }} }}