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arXiv · 2412.03129

Short-reach Optical Communications: A Real-world Task for Neuromorphic Hardware

Elias Arnold·Eike-Manuel Edelmann·Alexander von Bank·Eric Müller·Laurent Schmalen·Johannes Schemmel
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Abstract

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.

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Elias Arnold, Eike-Manuel Edelmann, Alexander von Bank, Eric Müller, Laurent Schmalen, Johannes Schemmel. 2024-12-04. Short-reach Optical Communications: A Real-world Task for Neuromorphic Hardware. https://arxiv.org/abs/2412.03129

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