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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.