• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2322-2324
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• 2003

In this paper, several network nodes that are essential elements of create a distributed system have designed and developed using CAN(Controller Area Network). The network nodes consist of RISC(Reduced Instruction Set Computer) CPU and CAN controller. By these two parts used, the network nodes have merits that are fast process speed, stable communication, cheapness and others. The most important quality that is the designed network node can be applied to various network control systems.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.141-142
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• 2009

The PEFP (Proton Engineering Frontier Project) is constructing a 100MeV proton Linac (Linear Accelerator). The 20 MeV 20 mA proton beam has been serviced for an application in the fields of material, biological, information technology and medical sciences. For a stable and efficient acceleration of a proton beam, the control requirements must be optimized by studying various control methods. We propose that the integrated control system for the Linac machine components must be based on a distribution control method to improve a centralized control system. Based on EPICS (Experimental Physics and Industrial Control System) real-time software, the VME (Versa Module European package format) IOC (Input Output Controller) was developed under cross development environment with a RISC (Reduced Instruction Set Computer) PowerPC system. In this paper, we describe the design and implementation of distributed control system using the VME-based EPICS middleware for various components of the large proton accelerator.

• The Magazine of the IEIE
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• v.25 no.5
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• pp.31-41
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• 1998

With the advent of portable electronic systems, power consumption has recently become a major issue in circuit and system design. Furthermore, the sophisticated fabrication technology makes it possible to embed more functions and features in a VLSI chip, consequently calling for both higher performance and lower power to deal with the ever growing complexity of system algorithms than in the past. VLSI designers should cope with two conflicting constraints, high performance and low power, offering an optimum trade off of these constraints to meet requirements of system. Historically, VLSI designers have focused on performance improvement, and power dissipation was not a design criteria but an afterthought. This design paradigm should be changed, as power is emerging as the most critical design constraint. In VLSI design, low power design can be accomplished through many ways, for instance, process, circuit/logic design, architectural design, and etc.. In this paper, a few low power design examples, which have been used in 8 bit micro-controller core, and can be used also in 4/16/32 bit micro-controller cores, are presented in the areas of circuit, logic and architectural design. We first propose a low power guidelines for micro-controller design in SAMSUNG, and more detailed design examples are followed applying 4 specific design guidelines. The 1st example shows the power reduction through reduction of number of state clocks per instruction. The 2nd example realized the power reduction by applying RISC(Reduced Instruction Set Computer) concept. The 3rd example is to optimize the algorithm for ALU(Arithmetic Logic Unit) to lower the power consumption, Lastly, circuit cells designed for low power are described.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.4B
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• pp.771-782
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• 2000

In this paper, an H.263 video codec is implemented by adopting the concept of hardware and software co-design. Each module of the codec is investigated to find which approach between hardware and software is better to achieve real-time processing speed as well as flexibility. The hardware portion includes motion-related engines, such as motion estimation and compensation, and a memory control part. The remaining portion of theH.263 video codec is implemented in software using a RISC processor. This paper also introduces efficient design methods for hardware and software modules. In hardware, an area-efficient architecture for the motion estimator of a multi-resolution block matching algorithm using multiple candidates and spatial correlation in motion vector fields (MRMCS), is suggested to reduce the chip size. Software optimization techniques are also explored by using the statistics of transformed coefficients and the minimum sum of absolute difference (SAD)obtained from the motion estimator.

• Journal of the Korea Society of Computer and Information
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• v.14 no.11
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• pp.97-103
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• 2009

In this thesis, we discuss the implementation of parallel processing system which is able to get a high degree of efficiency(size, cost, performance and flexibility) by using $Nios^{(R)}$ II(32bit RISC(Reduced Instruction Set Computer) processor) embedded processor in DE2-$70^{(R)}$ reference board. The designed Parallel processing system is master-slave, shared memory and MIMD(Mu1tiple Instruction-Multiple Data stream) architecture with 4-processor. For performance test of system, N-point FFT is used. The result is represented speed-up as follow; in the case of using 2-processor(core), speed-up is shown as average 1.8 times as 1-processor's. When 4-processor, the speed-up is shown as average 2.4 times as it's.

• The Magazine of the IEIE
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• v.28 no.7
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• pp.49-55
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• 2001

1970년대에 개발된 마이크로 프로세서는 제어기기 분야 및 소형 컴퓨터에서 주로 사용되어 오다가 1980년대에 이르러 RISC(Reduced Instruction Set Computer) 구조의 도입으로 중대형 컴퓨터에 이르기까지 광범위하게 사용되고 있다. 또한 반도체 기술의 급격한 발전으로 슈퍼스칼라 구조가 마이크로 프로세서에서도 적용되고 있으며 동작 속도도 수백 MHz에 이르고 있다. 마이크로 프로세서는 프로그램을 수행하기 위해서 프로그램과 데이터를 메모리로부터 읽어 와야 한다. 그런데 메모리 용량은 빠른 속도로 증가하고 있지만 동작 속도는 마이크로 프로세서의 동작 속도에 크게 미치지 못하고 있다. 1980년에 DRAM의 접근 속도는 250nsec이었으나 1998년에 RDRAM의 동작속도는 300MHz로 70여배 빨라졌다. 그러나 마이크로프로세서는 1980년에 8086의 동작 속도가 8MHz이던 것이 1998년에는 팬티엄-2가 500MHz에 이르고 있다. 더욱이 팬티엄-2는 슈퍼스칼라 구조이므로 이를 감안하면 1GHz 이상에 이르러 120여 배 빨라진 것을 알 수 있다. 이와 같은 메모리 속도와 마이크로 프로세서 속도 차이에 더하여, 메모리와 마이크로 프로세서를 인쇄 회로 기판에서 연결하는데 따른 물리적 특성은 변화하지 않으므로 데이터 전송 폭을 넓히는 것에는 한계가 있다. 따라서 향후 컴퓨터 성능 발달을 제한하는 주요 요소 중 하나는 마이크로 프로세서와 메모리 사이의 데이터 전송 폭이다. 프로그램과 데이터가 메모리에 저장되는 본 뉴먼 방식의 컴퓨터에서 데이터 전송 폭을 줄이기 위해서는 코드 밀도(Code Density)가 높은 컴퓨터 구조를 연구하는 것이 필요하다. 한편 마이크로 프로세서는 실장 제어용으로 거의 모든 전자 제품 및 자동화 기기에서 채용하고 있다. 특히 냉장고, 에어콘, 전축, TV, 세탁기 등 가전기기와 Fax, 복사기, 프린터 등 사무용기기와 자동차, 선박, 자동화기계 등 사무 및 산업용 기기와 PDA(휴대용 정보 기기), NC(Network Computer) 등 정보 기기 그리고 각종 오락기, 노래 반주지 등 정보 기기 등에서 사용하는 실장 제어용 마이크로 프로세서 시장은 매년 10% 이상씩 성장하고 있으며, 21세기 산업을 주도하는 핵심 기술로 자리 매김하고 있다. 이러한 실장 제어용 기기는 마이크로 프로세서와 메모리 및 입출력 자이가 하나의 반도체에 집적되는 경우가 많다. 그런데 반도체 가격은 반도체 크기에 따라 결정되며, 가장 넓은 면적을 차지하는 것은 메모리이다. 따라서 반도체 가격을 낮추기 위해서는 메모리 크기를 줄여야 하며, 이를 위해서 또한 코드 밀도가 높은 컴퓨터 구조에 대한 연구가 필요하다. 최근에는 322비트 RISC 명령어를 16비트 명령어로 축약한 구조가 연구되었다. ARM-7TDMI는 ARM-7의 16비트 축약 명령어 구조이며, TR4101은 MIPS-R3000의 16비트 축약 명령어 구조이다. 이들 16비트 축약 명령어 RISC는 종래 RISC와의 호환성을 위하여 2가지 모드로 동작하므로 구조가 복잡하고, 16비트 명령어에서는 8개의 레지스타만을 접근할 수 있으므로 성능이 크게 떨어지는 단점을 가진다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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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 }} }}