Hardware Realization of Nonlinear Activation Functions for NN-based Optical Equalizers

Sasipim Srivallapanondh, Pedro J. Freire, Antonio Napoli, Sergei K. Turitsyn, Jaroslaw E. Prilepsky
5/16/2023
cs.LGphysics.optics

No confident code implementation found for this paper.

Abstract

To reduce the complexity of the hardware implementation of neural network-based optical channel equalizers, we demonstrate that the performance of the biLSTM equalizer with approximated activation functions is close to that of the original model.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Multi-Task Learning to Enhance Generalizability of Neural Network Equalizers in Coherent Optical Systems

85% match2023

Real-Time FPGA Demonstrator of ANN-Based Equalization for Optical Communications

84% match2024

Reprogrammable Electro-Optic Nonlinear Activation Functions for Optical Neural Networks

84% match2 repos2792019

Fully-blind Neural Network Based Equalization for Severe Nonlinear Distortions in 112 Gbit/s Passive Optical Networks

83% match2024

Neural Network Architectures for Optical Channel Nonlinear Compensation in Digital Subcarrier Multiplexing Systems

83% match2023

CNN-Based Equalization for Communications: Achieving Gigabit Throughput with a Flexible FPGA Hardware Architecture

83% match2024

Cite this paper

@article{srivallapanondh2023hardware,
  title  = {Hardware Realization of Nonlinear Activation Functions for NN-based Optical Equalizers},
  author = {Sasipim Srivallapanondh and Pedro J. Freire and Antonio Napoli and Sergei K. Turitsyn and Jaroslaw E. Prilepsky},
  year   = {2023},
  eprint = {2305.09495},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2305.09495v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2305.09495.svg)](https://codeofpaper.com/paper/2305.09495)

Discussion