# Spiking Neural Network Nonlinear Demapping on Neuromorphic Hardware for IM/DD Optical Communication

## TL;DR

This paper demonstrates that spiking neural networks implemented on neuromorphic hardware can effectively perform nonlinear demapping in optical communication systems, outperforming traditional methods in software and with minimal hardware penalty.

## Contribution

The work introduces a neuromorphic hardware implementation of an SNN nonlinear demapper for optical links, showing superior performance over conventional equalization and neural network methods.

## Key findings

- SNN demapper outperforms linear and Volterra equalizers at 2e-3 BER.
- Hardware implementation incurs only 0.2 dB penalty.
- Neuromorphic SNN achieves better accuracy than software counterparts.

## Abstract

Neuromorphic computing implementing spiking neural networks (SNN) is a promising technology for reducing the footprint of optical transceivers, as required by the fast-paced growth of data center traffic. In this work, an SNN nonlinear demapper is designed and evaluated on a simulated intensity-modulation direct-detection link with chromatic dispersion. The SNN demapper is implemented in software and on the analog neuromorphic hardware system BrainScaleS-2 (BSS-2). For comparison, linear equalization (LE), Volterra nonlinear equalization (VNLE), and nonlinear demapping by an artificial neural network (ANN) implemented in software are considered. At a pre-forward error correction bit error rate of 2e-3, the software SNN outperforms LE by 1.5 dB, VNLE by 0.3 dB and the ANN by 0.5 dB. The hardware penalty of the SNN on BSS-2 is only 0.2 dB, i.e., also on hardware, the SNN performs better than all software implementations of the reference approaches. Hence, this work demonstrates that SNN demappers implemented on electrical analog hardware can realize powerful and accurate signal processing fulfilling the strict requirements of optical communications.