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

2024/12/04 by Elias Arnold, Arnold, Elias, Eike-Manuel Edelmann +9 · 1 citation
Engineering · Computer Science · #Advanced Memory and Neural Computing #Neural Networks and Reservoir Computing #Quantum Computing Algorithms and Architecture

paper · pdf · doi:10.48550/arxiv.2412.03129

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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• Encoding Optimization for Low-Complexity Spiking Neural Network Equalizers in IM/DD Systems

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