Short-reach Optical Communications: A Real-world Task for Neuromorphic Hardware
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913596950183936 |
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| author | Arnold, Elias Edelmann, Eike-Manuel von Bank, Alexander Müller, Eric Schmalen, Laurent Schemmel, Johannes |
| author_facet | Arnold, Elias Edelmann, Eike-Manuel von Bank, Alexander Müller, Eric Schmalen, Laurent Schemmel, Johannes |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2412_03129 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Short-reach Optical Communications: A Real-world Task for Neuromorphic Hardware Arnold, Elias Edelmann, Eike-Manuel von Bank, Alexander Müller, Eric Schmalen, Laurent Schemmel, Johannes Neural and Evolutionary Computing 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. |
| title | Short-reach Optical Communications: A Real-world Task for Neuromorphic Hardware |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2412.03129 |