• Title/Summary/Keyword: Hardware accelerator

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A SoC based on the Gaussian Pyramid (GP) for Embedded image Applications (임베디드 영상 응용을 위한 GP_SoC)

  • Lee, Bong-Kyu
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.59 no.3
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    • pp.664-668
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    • 2010
  • This paper presents a System-On-a-chip (SoC) for embedded image processing and pattern recognition applications that need Gaussian Pyramid structure. The system is fully implemented into Field-Programmable Gate Array (FPGA) based on the prototyping platform. The SoC consists of embedded processor core and a hardware accelerator for Gaussian Pyramid construction. The performance of the implementation is benchmarked against software implementations on different platforms.

The design of a fuzzy logic controller for the pointing loop of the spin-stabilized platform (자전 안정화 플랫트폼 위치제어용 퍼지 논리 제어기 설계)

  • 유인억;이상정
    • 제어로봇시스템학회:학술대회논문집
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    • 1992.10a
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    • pp.112-116
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    • 1992
  • In this paper, a fuzzy logic controller(FLC) is designed for the pointing loop of the spin-stabilized platform. For the fuzzy inference, a fuzzy accelerator board using the Togai InfraLogic software and digital fuzzy processor(DFP110FC) is designed, and a validation of an algorithm for fuzzy logic control is also presented. The pointing loop of the spin-stabilized platform using FLC has better performance of step responses than a proportional controller in case of same loop hain through the software simulation and the experiment of implemented hardware.

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Design and Implementation of Image-Pyramid

  • Lee, Bongkyu
    • Journal of Korea Multimedia Society
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    • v.19 no.7
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    • pp.1154-1158
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    • 2016
  • This paper presents a System-On-a-chip for embedded image processing applications that need Gaussian Pyramid structure. The system is fully implemented into Field-Programmable Gate Array (FPGA) based on the prototyping platform. The SoC consists of embedded processor core and a hardware accelerator for Gaussian Pyramid construction. The performance of the implementation is benchmarked against software implementations on different platforms.

The Implementation of Graphic Pipeline Simulator for 3D Graphic Accelerator Hardware Design (3차원 그래픽 가속 하드웨어 설계를 위한 그래픽 파이프라인 시뮬레이터 구현)

  • 이원종;박우찬;한탁돈
    • Proceedings of the Korean Information Science Society Conference
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    • 2000.10c
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    • pp.3-5
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    • 2000
  • 고성능의 3차원 그래픽 가속기 설계를 위해서는 어플리케이션, 하드웨어 구조, 수행모델 채택, 설계비용 등의 다양한 고려사항이 요구되고 따라서 각 모델에 따른 사전 시뮬레이션 환경구축은 반드시 필요하다. 이에 본 논문에서는 기본적인 3차원 그래픽 파이프라인 작업을 수행하여 다양한 결과를 보여주는 이식성 높은 시뮬레이션 환경을 제공함으로써 3차원 그래픽 가속하드웨어 세부모듈 설계에 필요한 설계 고려사항을 효과적으로 제시할 수 있게 하였다.

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A Study on The design of Accelerator of The Outlined Font Generation (고해상도 윤곽선 문자 발생가속기 설계에 대한 연구)

  • Seo, Ju-Ha;Ahn, Tae-Young
    • Journal of Industrial Technology
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    • v.11
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    • pp.55-63
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    • 1991
  • This paper presents a design of the accelerate circuit for the conversion of the vector font data into the bit-mapped image. Among the Bezier curve algorithm, the subdivision algorithm gives the good performance and easy hardware implementation. The sequencer is realized by the proprammable gate array and the processing unit is composed of EPLDs and TTL ICs.

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A SoC Based on a Neural Network for Embedded Smart Applications (임베디드 스마트 응용을 위한 신경망기반 SoC)

  • Lee, Bong-Kyu
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.58 no.10
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    • pp.2059-2063
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    • 2009
  • This paper presents a programmable System-On-a-chip (SoC) for various embedded smart applications that need Neural Network computations. The system is fully implemented into a prototyping platform based on Field Programmable Gate Array (FPGA). The SoC consists of an embedded processor core and a reconfigurable hardware accelerator for neural computations. The performance of the SoC is evaluated using a real image processing application, an optical character recognition (OCR) system.

Design of deep learning based hardware accelerator for digital watermarking (디지털 워터마킹을 위한 딥러닝 기반 하드웨어 가속기의 설계)

  • Lee, Jae-Eun;Seo, Young-Ho;Kim, Dong-Wook
    • Proceedings of the Korean Society of Broadcast Engineers Conference
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    • 2020.07a
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    • pp.544-545
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    • 2020
  • 본 논문에서는 영상 콘텐츠의 지적재산권 보호를 위하여 딥 러닝을 기반으로 하는 워터마킹 시스템 및 하드웨어 가속기 구조를 제안한다. 제안하는 워터마킹 시스템은 호스트 영상과 워터마크가 같은 해상도를 갖도록 변화시키는 전처리 네트워크, 전처리 네트워크를 거친 호스트 영상과 워터마크를 정합하여 워터마크를 삽입하는 네트워크, 그리고 워터마크를 추출하는 네트워크로 구성된다. 이 중 호스트 영상의 전처리 네트워크와 삽입 네트워크를 하드웨어로 설계한다.

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System-level simulation of CDMA mobile station modem ASIC (CDMA 이동국 모뎀 ASIC의 시스템 시뮬레이션)

  • 남형진;장경희;박경룡;김재석
    • Journal of the Korean Institute of Telematics and Electronics A
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    • v.33A no.6
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    • pp.220-229
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    • 1996
  • We presetn sytem-level simulation methodology as well as environment setup established for CDMA digtial cellular mobile station in an effort to verify CDMA modem ASIC design. To make the system-level simulation feasible, behavioral modeling of a microcontroller was first carried out with VHDL. In addition, models written in C language were also developed to provide ASIC with realistic input data. Finally, the netlist of CDMA modem ASIC was loaded on the a hardware accelerator, which was interfaced with VHDL simulator, and ismulation was performed by excuting the actual CDMA call processing software. Simulation resutls thus obtained were confirmed by comparing them with the emulation resutls from the actual system constructed on hardware modeler. these methods were proved to be effective in both discovering in advance malfunctions when embedded in the system or design errors of ASIC and reducing simulation time by a factor of as much as 20 in case of simulation at gate-level.

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Novel IME Instructions and their Hardware Architecture for Fast Search Algorithm (고속 탐색 알고리즘에 적합한 움직임 추정 전용 명령어 및 구조 설계)

  • Bang, Ho-Il;SunWoo, Myung-Hoon
    • Journal of the Institute of Electronics Engineers of Korea SD
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    • v.48 no.12
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    • pp.58-65
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    • 2011
  • This paper presents an ASIP (Application-specific Instruction Processor) for motion estimation that employs specific IME instructions and its programmable and reconfigurable hardware architecture for various video codecs, such as H.264/AVC, MPEG4, etc. With the proposed specific instructions and variable point 2D SAD hardware accelerator, it can handle the real-time processing requirement of High Definition (HD) video. With the SAD unit and its parallel operations using pattern information, the proposed IME instructions support not only full search algorithms but also other fast search algorithms. The hardware size is 25.5K gates for each Processing Element Group (PEG) which has 128 SAD Processor Elements (PEs). The proposed ASIP has been verified by the Synopsys Processor Designer and implemented by the Design Compiler using the IBM 90nm process technology. The hardware size is 453K gates for the IME unit and the operating frequency is 188MHz for 1080p@30 frame in real time. The proposed ASIP can reduce the hardware size about 26% and the number of operation cycles about 18%.

FPGA-Based Hardware Accelerator for Feature Extraction in Automatic Speech Recognition

  • Choo, Chang;Chang, Young-Uk;Moon, Il-Young
    • Journal of information and communication convergence engineering
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    • v.13 no.3
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    • pp.145-151
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    • 2015
  • We describe in this paper a hardware-based improvement scheme of a real-time automatic speech recognition (ASR) system with respect to speed by designing a parallel feature extraction algorithm on a Field-Programmable Gate Array (FPGA). A computationally intensive block in the algorithm is identified implemented in hardware logic on the FPGA. One such block is mel-frequency cepstrum coefficient (MFCC) algorithm used for feature extraction process. We demonstrate that the FPGA platform may perform efficient feature extraction computation in the speech recognition system as compared to the generalpurpose CPU including the ARM processor. The Xilinx Zynq-7000 System on Chip (SoC) platform is used for the MFCC implementation. From this implementation described in this paper, we confirmed that the FPGA platform is approximately 500× faster than a sequential CPU implementation and 60× faster than a sequential ARM implementation. We thus verified that a parallelized and optimized MFCC architecture on the FPGA platform may significantly improve the execution time of an ASR system, compared to the CPU and ARM platforms.