Efficient FPGA Implementation of an Optimized SNN-based DFE for Optical Communications
Abstract
The ever-increasing demand for higher data rates in communication systems intensifies the need for advanced non-linear equalizers capable of higher performance. Recently artificial neural networks (ANNs) were introduced as a viable candidate for advanced non-linear equalizers, as they outperform traditional methods. However, they are computationally complex and therefore power hungry. Spiking neural networks (SNNs) started to gain attention as an energy-efficient alternative to ANNs. Recent works proved that they can outperform ANNs at this task. In this work, we explore the design space of an SNN-based decision-feedback equalizer (DFE) to reduce its computational complexity for an efficient implementation on field programmable gate array (FPGA). Our Results prove that it achieves higher communication performance than ANN-based DFE at roughly the same throughput and at 25X higher energy efficiency.
Keywords
Cite
@article{arxiv.2409.08698,
title = {Efficient FPGA Implementation of an Optimized SNN-based DFE for Optical Communications},
author = {Mohamed Moursi and Jonas Ney and Bilal Hammoud and Norbert Wehn},
journal= {arXiv preprint arXiv:2409.08698},
year = {2024}
}
Comments
accepted for publication in IEEE Middle East Conference on Communications and Networking (MECOM 2024). November, 2024