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

TL;DR

This paper demonstrates that neuromorphic hardware implementing spiking neural networks can effectively perform real-world optical communication tasks, highlighting energy efficiency and practical advantages over traditional algorithms.

Contribution

It introduces a real-world optical communication task suitable for neuromorphic hardware, showing its potential for energy-efficient signal processing in data centers.

Findings

Small-scale SNNs achieve required accuracy

Task is inherently time-dependent and suitable for SNNs

Facilitates optimization for energy and resource efficiency

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.