67‐1:  Distinguished Paper:  Efficient Multi‐Quality Super Resolution Using a Deep Convolutional Neural Network for an FPGA Implementation | Zendy

Kim Min Beom | Zendy; Lee Sanglyn | Zendy; Kim Ilho | Zendy; Hong Hee Jung | Zendy; Kim Chang Gone | Zendy; Yoon Soo Young | Zendy

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67‐1: Distinguished Paper: Efficient Multi‐Quality Super Resolution Using a Deep Convolutional Neural Network for an FPGA Implementation

Author(s) -

Kim Min Beom,

Lee Sanglyn,

Kim Ilho,

Hong Hee Jung,

Kim Chang Gone,

Yoon Soo Young

Publication year - 2020

Publication title -

sid symposium digest of technical papers

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.351

H-Index - 44

eISSN - 2168-0159

pISSN - 0097-966X

DOI - 10.1002/sdtp.14039

Subject(s) - convolutional neural network , computer science , field programmable gate array , trimming , quality (philosophy) , artificial neural network , artificial intelligence , resolution (logic) , application specific integrated circuit , deep learning , computer architecture , pattern recognition (psychology) , embedded system , operating system , philosophy , epistemology

We propose an efficient deep convolutional neural network for a super‐resolution which is capable of multiple‐quality input, by analyzing the input quality and choosing appropriate features automatically. To implement the network in an FPGA and an ASIC, we employ a network trimming technique to compress the neural network.

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