• Title/Summary/Keyword: Mobile GPU

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Analysis of Job Scheduling and the Efficiency for Multi-core Mobile GPU (멀티코어형 모바일 GPU의 작업 분배 및 효율성 분석)

  • Lim, Hyojeong;Han, Donggeon;Kim, Hyungshin
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.15 no.7
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    • pp.4545-4553
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    • 2014
  • Mobile GPU has led to the rapid development of smart phone graphic technology. Most recent smart phones are equipped with high-performance multi-core GPU. How a multi-core mobile GPU can be utilized efficiently will be a critical issue for improving the smart phone performance. On the other hand, most current research has focused on a single-core mobile GPU; studies of multi-core mobile GPU are rare. In this paper, the job scheduling patterns and the efficiency of multi-core mobile GPU are analyzed. In the profiling result, despite the higher number of GPU cores, the total processing time required for certain graphics applications were increased. In addition, when GPU is processing for 3D games, a substantial amount of overhead is caused by communication between not only the CPU and GPU, but also within the GPUs. These results confirmed that more active research for multi-core mobile GPU should be performed to optimize the present mobile GPUs.

Trends of Mobile GPU (모바일 GPU 동향)

  • Han, J.H.;Byun, J.G.;Eum, N.W.
    • Electronics and Telecommunications Trends
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    • v.28 no.2
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    • pp.50-57
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    • 2013
  • 스마트폰 및 태블릿 PC에 들어가는 핵심 부품인 AP(Application Processor)는 모두 GPU(Graphics Processing Unit)를 내장하고 있다. 이는 칩 면적의 제약과 사용 가능한 전력의 한계로 데스크톱의 그래픽 카드에 탑재된 고성능 GPU와는 다른 설계 제약을 받는다. 본고에서는 고성능 GPU와 다른 설계 조건을 갖는 mobile GPU 기술에 대해서 알아보았고 대표적인 commercial mobile GPU인 Imagination, ARM, Qualcomm, NVidia사의 mobile GPU의 특징 및 성능에 대해서 알아보았다.

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Localization and Autonomous Navigation Using GPU-based SIFT and Virtual Force for Mobile Robots (GPU 기반 SIFT 방법과 가상의 힘을 이용한 이동 로봇의 위치 인식 및 자율 주행 제어)

  • Tak, Myung Hwan;Joo, Young Hoon
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.65 no.10
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    • pp.1738-1745
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    • 2016
  • In this paper, we present localization and autonomous navigation method using GPU(Graphics Processing Unit)-based SIFT(Scale-Invariant Feature Transform) algorithm and virtual force method for mobile robots. To do this, at first, we propose the localization method to recognize the landmark using the GPU-based SIFT algorithm and to update the position using extended Kalman filter. And then, we propose the A-star algorithm for path planning and the virtual force method for autonomous navigation of the mobile robot. Finally, we demonstrate the effectiveness and applicability of the proposed method through some experiments using the mobile robot with OPRoS(Open Platform for Robotic Services).

Implementation of IQ/IDCT in H.264/AVC Decoder Using GP-GPU (GP-GPU를 이용한 H.264/AVC 디코더의 IQ/IDCT구현)

  • Jeong, Jun-Mo;Lee, Kwang-Yeob
    • Journal of IKEEE
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    • v.14 no.2
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    • pp.76-81
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    • 2010
  • The need for dedicated hardware continue to decrease as the mobile CPU's performance increases. But, there is a limit to a mobile CPU's performance. GP-GPU(General-Purpose computing on Graphics Processing Units) can improve performance without adding other dedicated hardware. This paper presents the implementation of Inverse Quantization, Inverse DCT and Color Space Conversion module in H.264/AVC decoder using GP-GPU for a mobile environments. The proposed architecture improves approximately 40% of performance when it use all the features.

Multiview Stereo Matching on Mobile Devices Using Parallel Processing on Embedded GPU (임베디드 GPU에서의 병렬처리를 이용한 모바일 기기에서의 다중뷰 스테레오 정합)

  • Jeon, Yun Bae;Park, In Kyu
    • Journal of Broadcast Engineering
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    • v.24 no.6
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    • pp.1064-1071
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    • 2019
  • Multiview stereo matching algorithm is used to reconstruct 3D shape from a set of 2D images. Conventional multiview stereo algorithms have been implemented on high-performance hardware due to the heavy complexity that contains a large number of calculations in each step. However, as the performance of mobile graphics processors has recently increased rapidly, complex computer vision algorithms can now be implemented on mobile devices like a smartphone and an embedded board. In this paper we parallelize an multiview stereo algorithm using OpenCL on mobile GPU and provide various optimization techniques on the embedded hardware with limited resource.

Performance Analysis and Optimization of mobile GPU in Android Phone (안드로이드 폰에서의 모바일 GPU 성능 분석 및 최적화)

  • Cho, Chang-Woo;Ashok, Sharma;Kim, Shin-Dug
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2013.07a
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    • pp.1-4
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    • 2013
  • 본 논문에서는 최신 안드로이드 기반 상용 스마트폰의 모바일 GPU 성능 향상을 위한 방법론을 제안한다. 동일 하드웨어를 가지고 스마트폰을 개발하더라도 제조사의 역량에 따라 소프트웨어 최적화의 정도가 달라서 그래픽 성능 차이가 날 수 있다. 그러므로 우리는 시스템 소프트웨어 레벨에서 그래픽 품질에 아무런 영향을 주지 않고 성능 향상을 이끌어낼 수 있는 기법에 대해 소개한다. 이를 위해 A사, B사 안드로이드 스마트폰을 대상으로 안드로이드 커널에 따른 분석을 수행하였고, GPU 디바이스 드라이버에 따른 분석을 수행하였으며, 마지막으로 타사 휴대폰과의 성능 비교를 통해 이 결과를 비교 분석하였다. 결과적으로 GPU 디바이스 드라이버 변경과 안드로이드 커널 변경을 시도함으로써 B사 대비 68%의 성능을 보인 A사 스마트폰의 성능을 96%까지 향상시킬 수 있었다.

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Efficient Convolutional Neural Network with low Complexity (저연산량의 효율적인 콘볼루션 신경망)

  • Lee, Chanho;Lee, Joongkyung;Ho, Cong Ahn
    • Journal of IKEEE
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    • v.24 no.3
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    • pp.685-690
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    • 2020
  • We propose an efficient convolutional neural network with much lower computational complexity and higher accuracy based on MobileNet V2 for mobile or edge devices. The proposed network consists of bottleneck layers with larger expansion factors and adjusted number of channels, and excludes a few layers, and therefore, the computational complexity is reduced by half. The performance the proposed network is verified by measuring the accuracy and execution times by CPU and GPU using ImageNet100 dataset. In addition, the execution time on GPU depends on the CNN architecture.

GPU based Fast Recognition of Artificial Landmark for Mobile Robot (주행로봇을 위한 GPU 기반의 고속 인공표식 인식)

  • Kwon, Oh-Sung;Kim, Young-Kyun;Cho, Young-Wan;Seo, Ki-Sung
    • Journal of the Korean Institute of Intelligent Systems
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    • v.20 no.5
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    • pp.688-693
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    • 2010
  • Vision based object recognition in mobile robots has many issues for image analysis problems with neighboring elements in dynamic environments. SURF(Speeded Up Robust Features) is the local feature extraction method of the image and its performance is constant even if disturbances, such as lighting, scale change and rotation, exist. However, it has a difficulty of real-time processing caused by representation of high dimensional vectors. To solve th problem, execution of SURF in GPU(Graphics Processing Unit) is proposed and implemented using CUDA of NVIDIA. Comparisons of recognition rates and processing time for SURF between CPU and GPU by variation of robot velocity and image sizes is experimented.

Realtime Rendering of Water Surface using GPU (GPU를 이용한 물 표면 실시간 렌더러 구현)

  • Lee, JaeSung;Kwon, Dukho;Sung, Mankyu
    • Proceedings of the Korea Information Processing Society Conference
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    • 2017.04a
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    • pp.1008-1009
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    • 2017
  • 본 연구는 게임을 비롯한 많은 콘텐츠에서 활용하기 위한 GPU기반 사실적 물 애니메이션 기법을 제안한다. 물 표면은 반사 및 투사와 같은 물리적 현상이 일어나며, 시점에 따른 반사와 투사의 정도가 자동적으로 조절되어야 한다. 본 논문에서는 GPU 프레임 버퍼를 이용한 렌더투텍스처 방법을 이용하여 반사 및 투사결과를 텍스처로 저장하였으며, 이 저장된 데이터에 대한 UV좌표 값을 변경함으로서, 자연스러운 물결의 모습을 표현하였다. 또한 투사 및 반사의 정도가 프레넬(Fresnel) 공식을 통해 자동적으로 계산되도록 하였다.

Earliest Virtual Deadline Zero Laxity Scheduling for Improved Responsiveness of Mobile GPUs

  • Choi, Seongrim;Cho, Suhwan;Park, Jonghyun;Nam, Byeong-Gyu
    • JSTS:Journal of Semiconductor Technology and Science
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    • v.17 no.1
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    • pp.162-166
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    • 2017
  • Earliest virtual deadline zero laxity (EVDZL) algorithm is proposed for mobile GPU schedulers for its improved responsiveness. Responsiveness of user interface (UI) is one of the key factors in evaluating smart devices because of its significant impacts on user experiences. However, conventional GPU schedulers based on completely fair scheduling (CFS) shows a poor responsiveness due to its algorithmic complexity. In this letter, we present the EVDZL scheduler based on the conventional earliest deadline zero laxity (EDZL) algorithm by accommodating the virtual laxity concept into the scheduling. Experimental results show that the EVDZL scheduler improves the response time of the Android UI by 9.6% compared with the traditional CFS scheduler.