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1
FPGA-based Trainable Autoencoder for Communication Systems:
, In:
Proceedings of the 2022 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays
,
Ney, Jonas
;
Dörner, Sebastian
;
Herrmann, Matthias
... - p. 154 ff. , 2022
Link:
https://dl.acm.org/doi/10.1145/3490422.3502337
RT T1 <cTitle>Proceedings of the 2022 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays</cTitle>: T1 <Title>FPGA-based Trainable Autoencoder for Communication Systems</Title> UL https://suche.suub.uni-bremen.de/peid=acm-3502337&Exemplar=1&LAN=DE A1 Ney, Jonas A1 Dörner, Sebastian A1 Herrmann, Matthias A1 Hassani Sadi, Mohammad A1 Clausius, Jannis A1 ten Brink, Stephan A1 Wehn, Norbert PB ACM YR 2022 K1 ann K1 autoencoder K1 backpropagation K1 communication K1 fpga K1 Hardware K1 Integrated circuits K1 Reconfigurable logic and FPGAs K1 Hardware accelerators K1 Applied computing K1 Physical sciences and engineering K1 Telecommunications K1 Computing methodologies K1 Machine learning K1 Machine learning approaches K1 Neural networks SP 154 ff. LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3490422.3502337 DO https://dl.acm.org/doi/10.1145/3490422.3502337 SF ELIB - SuUB Bremen
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