  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.
