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

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

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 comp...

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Veröffentlicht in:SID International Symposium Digest of technical papers 2020-08, Vol.51 (1), p.993-996
Hauptverfasser: Kim, Min Beom, Lee, Sanglyn, Kim, Ilho, Hong, Hee Jung, Kim, Chang Gone, Yoon, Soo Young
Format: Artikel
Sprache:eng
Schlagworte:
Artificial neural networks
Deep-learning
Neural networks
Super-resolution
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Zusammenfassung: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.
ISSN:0097-966X
2168-0159
DOI:10.1002/sdtp.14039