• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.11a
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• pp.349-350
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• 2022

Recently, AI (Artificial Intelligence) has been applied to various technologies such as automatic driving, robot and smart communication. Currently, AI system is developed by software-based method using tensor flow, and GPU (Graphic Processing Unit) is employed for processing unit. In this work, we developed an FPGA-based (Field Programmable Gate Array) AI system , and report on image recognition system to realize the AI system.

• Nuclear Engineering and Technology
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• v.48 no.5
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• pp.1192-1205
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• 2016

Field programmable gate array (FPGA)-based systems are thought to be a practical option to replace certain obsolete instrumentation and control systems in nuclear power plants. An FPGA is a type of integrated circuit, which is programmed after being manufactured. FPGAs have some advantages over other electronic technologies, such as analog circuits, microprocessors, and Programmable Logic Controllers (PLCs), for nuclear instrumentation and control, and safety system applications. However, safety-related issues for FPGA-based systems remain to be verified. Owing to this, modeling FPGA-based systems for safety assessment has now become an important point of research. One potential methodology is the dynamic flowgraph methodology (DFM). It has been used for modeling software/hardware interactions in modern control systems. In this paper, FPGA logic was analyzed using DFM. Four aspects of FPGAs are investigated: the "IEEE 1164 standard," registers (D flip-flops), configurable logic blocks, and an FPGA-based signal compensator. The ModelSim simulations confirmed that DFM was able to accurately model those four FPGA properties, proving that DFM has the potential to be used in the modeling of FPGA-based systems. Furthermore, advantages of DFM over traditional reliability analysis methods and FPGA simulators are presented, along with a discussion of potential issues with using DFM for FPGA-based system modeling.

• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.2
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• pp.7-16
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• 2019

Neural networks can be implemented in variety of ways, and specialized chips is being developed for hardware improvement. In order to apply such neural networks to wearable devices, the compactness and the low power operation are essential. In this point of view, a suitable implementation method is a digital circuit design using field programmable gate array (FPGA). To implement this system, the learning algorithm which takes up a large part in neural networks must be implemented within FPGA for better performance. In this paper, a back propagation algorithm among various learning algorithms is implemented using FPGA, and this neural network is verified by OR gate operation. In addition, it is confirmed that this neural network can be used to analyze various users' bio signal measurement results by learning algorithm.

• Journal of Power Electronics
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• v.10 no.3
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• pp.228-234
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• 2010

This paper investigates the conducted-noise characteristics of a digitally-controlled randomly-switched dc-dc converter. In order to investigate the effect of the suggested digital controller on the conducted-noise characteristics of a dc-dc converter, three factors have been studied: the field-programmable gate array (FPGA) clock speed, the randomization ratio percentage, and the effect of using a closed loop feedback controller. A field-programmable gate array is much more flexible than analog control circuits, has a lower cost, and can be used for power supply applications. A novel FPGA-based implementation has been suggested for obtaining the experimental validations and realizing the studied concepts. Furthermore, the experimental results have been discussed and design guidelines have been included.

• Nuclear Engineering and Technology
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• v.50 no.5
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• pp.780-787
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• 2018

This article presents a security module based on a field programmable gate array (FPGA) to mitigate man-in-the-middle cyber attacks. Nowadays, the FPGA is considered to be the state of the art in nuclear power plants I&C systems due to its flexibility, reconfigurability, and maintainability of the FPGA technology; it also provides acceptable solutions for embedded computing applications that require cybersecurity. The proposed FPGA-based security module is developed to mitigate information-gathering attacks, which can be made by gaining physical access to the network, e.g., a man-in-the-middle attack, using a cryptographic process to ensure data confidentiality and integrity and prevent injecting malware or malicious data into the critical digital assets of a nuclear power plant data communication system. A model-based system engineering approach is applied. System requirements analysis and enhanced function flow block diagrams are created and simulated using CORE9 to compare the performance of the current and developed systems. Hardware description language code for encryption and serial communication is developed using Vivado Design Suite 2017.2 as a programming tool to run the system synthesis and implementation for performance simulation and design verification. Simple windows are developed using Java for physical testing and communication between a personal computer and the FPGA.

• Journal of Digital Contents Society
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• v.10 no.4
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• pp.633-637
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• 2009

In this paper, we present the reuse module library generating algorithm and register-transfer (RT) library generating algorithm considering the power consumption of reuse module for field-programmable gate array (FPGA) technology mapping in order to implement into the circuit for calculating power consumption. To realize the circuit of calculation of power consumption, the FPGA is selected. Considering lookup table (LUT) conditions of selected FPGA, technology mapping process is conducted to minimize the total power consumption. With these information, the circuit is realized using suitable given power consumption among allocated results of modules.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.1
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• pp.11-19
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• 2024

The increased utilization of the FFT in signal processing, cryptography, and various other fields has highlighted the importance of optimization. In this paper, we propose the implementation of an accelerator that processes the radix-2 16 points FFT algorithm more rapidly and efficiently than FFT implementation of existing studies, using FPGA(Field Programmable Gate Array) hardware. Leveraging the hardware advantages of FPGA, such as parallel processing and pipelining, we design and implement the FFT logic in the PL (Programmable Logic) part using the Verilog language. We implement the FFT using only the Zynq processor in the PS (Processing System) part, and compare the computation times of the implementation in the PL and PS part. Additionally, we demonstrate the efficiency of our implementation in terms of computation time and resource usage, in comparison with related works.

• Journal of the Korean Society for Nondestructive Testing
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• v.35 no.6
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• pp.389-397
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• 2015

Recently, wave propagation imaging based on laser scanning-generated elastic waves has been intensively used for nondestructive inspection. However, the proficiency of the conventional software based system reduces when the scan area is large since the processing time increases significantly due to unavoidable processor multitasking, where computing resources are shared with multiple processes. Hence, the field programmable gate array (FPGA) was introduced for a wave propagation imaging method in order to obtain extreme processing time reduction. An FPGA board was used for the design, implementing post-processing ultrasonic wave propagation imaging (UWPI). The results were compared with the conventional system and considerable improvement was observed, with at least 78% (scanning of $100{\times}100mm^2$ with 0.5 mm interval) to 87.5% (scanning of $200{\times}200mm^2$ with 0.5 mm interval) less processing time, strengthening the claim for the research. This new concept to implement FPGA technology into the UPI system will act as a break-through technology for full-scale automatic inspection.

• International Journal of Control, Automation, and Systems
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• v.4 no.5
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• pp.567-574
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• 2006

This paper presents the hardware implementation of a neural network controller for a nonlinear system with a micro-controller unit (MCU) and a field programmable gate array (FPGA) chip. As an on-line learning algorithm of a neural network, the reference compensation technique has been implemented on an MCU, while PID controllers with other functions such as counters and PWM generators are implemented on an FPGA chip. Interface between an MCU and a field programmable gate array (FPGA) chip has been developed to complete hardware implementation of a neural controller. The developed neural control hardware has been tested for balancing the inverted pendulum while controlling a desired trajectory of a cart as a nonlinear system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.9
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• pp.2071-2077
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• 2010

Interface modules design between image sensor and external components are designed by FPGA (Field Programmable Gate Array) in this paper. Generally speaking, to satisfy synchronization for the poor quality data in image, SRAM is needed. To receive synchronization signal and image signal data with pixel dimension, the proposed interface logic technique is implemented. From the proposed technique, we can obtain more clear screen by implementing with pixel dimension. Operating frequency of image sensor and that of TFT-LCD are 50MHz and 6.5MHz, respectively. Most of control logic functions are embedded in FPGA. The designed logic gate counter has 33,216 and is designed by Quartus II.