Computational Complexity Optimization of Neural Network-Based Equalizers in Digital Signal Processing: A Comprehensive Approach

Abstract

Experimental results based on offline processing reported at optical conferences increasingly rely on neural network-based equalizers for accurate data recovery. However, achieving low-complexity implementations that are efficient for real-time digital signal processing remains a challenge. This paper addresses this critical need by proposing a systematic approach to designing and evaluating low-complexity neural network equalizers. Our approach focuses on three key phases: training, inference, and hardware synthesis. We provide a comprehensive review of existing methods for reducing complexity in each phase, enabling informed choices during design. For the training and inference phases, we introduce a novel methodology for quantifying complexity. This includes new metrics that bridge software-to-hardware considerations, revealing the relationship between complexity and specific neural network architectures and hyperparameters. We guide the calculation of these metrics for both feed-forward and recurrent layers, highlighting the appropriate choice depending on the application's focus (software or hardware). Finally, to demonstrate the practical benefits of our approach, we showcase how the computational complexity of neural network equalizers can be significantly reduced and measured for both teacher (biLSTM+CNN) and student (1D-CNN) architectures in different scenarios. This work aims to standardize the estimation and optimization of computational complexity for neural networks applied to real-time digital signal processing, paving the way for more efficient and deployable optical communication systems.

Publication DOI: https://doi.org/10.1109/JLT.2024.3386886
Divisions: College of Engineering & Physical Sciences > Aston Institute of Photonics Technology (AIPT)
Aston University (General)
Additional Information: This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
Uncontrolled Keywords: Artificial neural networks,Equalizers,Hardware,Measurement,Neural networks,Signal processing,Task analysis,Training,computational complexity,hardware estimation,nonlinear equalizer,signal processing,Atomic and Molecular Physics, and Optics
Publication ISSN: 1558-2213
Last Modified: 13 Dec 2024 08:27
Date Deposited: 15 Apr 2024 16:50
Full Text Link:
Related URLs: https://ieeexpl ... ument/10496171/ (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2024-06-15
Published Online Date: 2024-04-10
Accepted Date: 2024-04-05
Authors: Freire, Pedro
Srivallapanondh, Sasipim
Spinnler, Bernhard
Napoli, Antonio
Costa, Nelson
Prilepsky, Jaroslaw E. (ORCID Profile 0000-0002-3035-4112)
Turitsyn, Sergei K. (ORCID Profile 0000-0003-0101-3834)

Export / Share Citation


Statistics

Additional statistics for this record