• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.1
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• pp.76-84
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• 2009

This paper proposes a SOC platform which is eligible for education and application SOC design. The platform, fully synthesizable and reconfigurable, includes the OpenRISC embedded processor, some basic peripherals such as GPIO, UART, debug interlace, VGA controller and WISHBONE interconnect. The platform uses a set of development environment such as compiler, assembler, debugger and RTOS that is built for HW/SW system debugging and software development. Designed SOC, IPs and Testbenches are described in the Verilog HDL and verified using commercial logic simulator, GNU SW development tool kits and the FPGA. Finally, a multimedia SOC derived from the SOC platform is implemented to ASIC using the Magnachip cell library based on 0.18um 1-poly 6-metal technology.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.11
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• pp.57-65
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• 2014

This paper presents a framework that is based on a reconfigurable macro-model of current-mode logic (CML) high-speed digital circuits enabling equation-based design optimization. The proposed macro-model is compatible with geometric programming, thereby enabling constraint-driven top-level power optimization. The proposed optimization framework is applied to a design of CML based serial-link transmitter with user-defined design specifications as an example of high speed digital circuits using 45nm and 90nm CMOS technology. The proposed optimization framework can derive a design with optimal power efficiency for given transistor technology nodes.

• 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 }} }}

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.1
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• pp.21-31
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• 2017

In this paper, the Zynq system-on-chip (SoC) platform is used to design an adaptive noise reduction and edge-detection system using partial reconfiguration. Filters are implemented in a partially reconfigurable (PR) region to provide high computational complexity in real-time, 1080p video processing. In addition, partial reconfiguration enables better utilization of hardware resources in the embedded system from autonomous replacement of filters in the same PR region. The proposed edge-detection system performs adaptive noise reduction if the noise density level in the incoming video sequences exceeds a given threshold value. Results of implementation show that the proposed system improves the accuracy of edge-detection results (14~20 times in Pratt's Figure of Merit) through self-reconfiguration of filter bitstreams triggered by noise density level in the video sequences. In addition, the ZyCAP controller implemented in this paper enables about 2.1 times faster reconfiguration when compared to a PCAP controller.