Embedded GPU Cluster Computing Framework for Inference of Convolutional Neural Networks

Kain, Evan T.; Wildenstein, Diego; Pineda, Andrew C.

doi:10.1109/hpec.2019.8916253

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…Architecture framework and strategy [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25] Scheduling and communication [26][27][28][29] Image processing and computer vision [30][31][32][33][34][35][36][37][38][39][40] Medical or health [41][42][43][44] Modeling or prediction [45][46][47][48][49][50][51] Convolution or performance analysis [6,[52][53][54] VLSI placement [55] GPU-based Machine Learning Technologies for EI Architecture platform [56][57][58][59]…”

Section: Gpu-based Deep Learning Technologies For Eimentioning

confidence: 99%

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GPU-Based Embedded Intelligence Architectures and Applications

Ang

Seng

2021

Electronics

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This paper present contributions to the state-of-the art for graphics processing unit (GPU-based) embedded intelligence (EI) research for architectures and applications. This paper gives a comprehensive review and representative studies of the emerging and current paradigms for GPU-based EI with the focus on the architecture, technologies and applications: (1) First, the overview and classifications of GPU-based EI research are presented to give the full spectrum in this area that also serves as a concise summary of the scope of the paper; (2) Second, various architecture technologies for GPU-based deep learning techniques and applications are discussed in detail; and (3) Third, various architecture technologies for machine learning techniques and applications are discussed. This paper aims to give useful insights for the research area and motivate researchers towards the development of GPU-based EI for practical deployment and applications.

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Section: Gpu-based Deep Learning Technologies For Eimentioning

confidence: 99%

“…The authors in [37] proposed an embedded GPU cluster computing approach for the inference of CNN. Their work involved the use of pre-trained CNN models developed by the U.S. Air Force Research Laboratory.…”

Section: Image Processing and Computer Visionmentioning

confidence: 99%