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FPGA-based systolic deconvolution architecture for upsampling

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Raj, Alex Noel Joseph, Cai, Lianhong, Li, Wei, Zhuang, Zhemin and Tjahjadi, Tardi (2022) FPGA-based systolic deconvolution architecture for upsampling. PeerJ Computer Science, 8 . e973. doi:10.7717/peerj-cs.973

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Official URL: https://doi.org/10.7717/peerj-cs.973

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Abstract

A deconvolution accelerator is proposed to upsample n × n input to 2n × 2n output by convolving with a k × k kernel. Its architecture avoids the need for insertion and padding of zeros and thus eliminates the redundant computations to achieve high resource efficiency with reduced number of multipliers and adders. The architecture is systolic and governed by a reference clock, enabling the sequential placement of the module to represent a pipelined decoder framework. The proposed accelerator is implemented on a Xilinx XC7Z020 platform, and achieves a performance of 3.641 giga operations per second (GOPS) with resource efficiency of 0.135 GOPS/DSP for upsampling 32 × 32 input to 256 × 256 output using a 3 × 3 kernel at 200 MHz. Furthermore, its high peak signal to noise ratio of almost 80 dB illustrates that the upsampled outputs of the bit truncated accelerator are comparable to IEEE double precision results.

Item Type: Journal Article
Subjects: Q Science > Q Science (General)
T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Science, Engineering and Medicine > Engineering > Engineering
Library of Congress Subject Headings (LCSH): Image processing -- Digital techniques, Image processing -- Mathematics, Deep learning (Machine learning), Field programmable gate arrays
Journal or Publication Title: PeerJ Computer Science
Publisher: PeerJ
ISSN: 2376-5992
Official Date: 2022
Dates:
DateEvent
2022Published
11 May 2022Available
14 April 2022Accepted
Volume: 8
Article Number: e973
DOI: 10.7717/peerj-cs.973
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
NTF17016Shantou Universityhttp://dx.doi.org/10.13039/100009047
82071992[NSFC] National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809
2020B1515120061Basic and Applied Basic Research Foundation of Guangdong ProvinceUNSPECIFIED
2020YFC0122103Key Technologies Research and Development Programhttp://dx.doi.org/10.13039/501100012165
2019KZDZX1013Guangdong Science and Technology Departmenthttp://dx.doi.org/10.13039/501100007162

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