From Algorithm to Implementation: Enabling High-Throughput CNN-Based Equalization on FPGA for Optical Communications-Reference-Cited by-同舟云学术

From Algorithm to Implementation: Enabling High-Throughput CNN-Based Equalization on FPGA for Optical Communications

Published:2023 Issue: Volume: Page:158-173
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
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Author:

Ney Jonas,Füllner Christoph,Lauinger Vincent,Schmalen Laurent,Randel Sebastian,Wehn Norbert

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-46077-7_11

Reference15 articles.

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2. Araujo, A., Norris, W.D., Sim, J.: Computing receptive fields of convolutional neural networks. Distill 4(11), e21 (2019)

3. Bayvel, P., et al.: Maximizing the optical network capacity. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 374(2062), 20140440 (2016)

4. Eriksson, T.A., Bülow, H., Leven, A.: Applying neural networks in optical communication systems: possible pitfalls. IEEE Photonics Technol. Lett. 29(23), 2091–2094 (2017). https://doi.org/10.1109/LPT.2017.2755663

5. Freire, P.J., et al.: Implementing neural network-based equalizers in a coherent optical transmission system using field-programmable gate arrays (2022)

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