Spiking neural networks (SNNs) emulated on dedicated neuromorphic accelerators promise to offer energy-efficient signal processing. However, the neuromorphic advantage over traditional algorithms still remains to be demonstrated in real-world applications. Here, we describe an intensity-modulation, direct-detection (IM/DD) task that is relevant to high-speed optical communication systems used in data centers. Compared to other machine learning-inspired benchmarks, the task offers several advantages. First, the dataset is inherently time-dependent, i.e., there is a time dimension that can be natively mapped to the dynamic evolution of SNNs. Second, small-scale SNNs can achieve the target accuracy required by technical communication standards. Third, due to the small scale and the defined target accuracy, the task facilitates the optimization for real-world aspects, such as energy efficiency, resource requirements, and system complexity.
@article{arxiv.2412.03129,
title = {Short-reach Optical Communications: A Real-world Task for Neuromorphic Hardware},
author = {Elias Arnold and Eike-Manuel Edelmann and Alexander von Bank and Eric Müller and Laurent Schmalen and Johannes Schemmel},
journal= {arXiv preprint arXiv:2412.03129},
year = {2024}
}