Efficient FPGA Implementation of an Optimized SNN-based DFE for Optical Communications
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866912026591232000 |
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| author | Moursi, Mohamed Ney, Jonas Hammoud, Bilal Wehn, Norbert |
| author_facet | Moursi, Mohamed Ney, Jonas Hammoud, Bilal Wehn, Norbert |
| contents | The ever-increasing demand for higher data rates in communication systems intensifies the need for advanced non-linear equalizers capable of higher performance. Recently artificial neural networks (ANNs) were introduced as a viable candidate for advanced non-linear equalizers, as they outperform traditional methods. However, they are computationally complex and therefore power hungry. Spiking neural networks (SNNs) started to gain attention as an energy-efficient alternative to ANNs. Recent works proved that they can outperform ANNs at this task. In this work, we explore the design space of an SNN-based decision-feedback equalizer (DFE) to reduce its computational complexity for an efficient implementation on field programmable gate array (FPGA). Our Results prove that it achieves higher communication performance than ANN-based DFE at roughly the same throughput and at 25X higher energy efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_08698 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Efficient FPGA Implementation of an Optimized SNN-based DFE for Optical Communications Moursi, Mohamed Ney, Jonas Hammoud, Bilal Wehn, Norbert Signal Processing The ever-increasing demand for higher data rates in communication systems intensifies the need for advanced non-linear equalizers capable of higher performance. Recently artificial neural networks (ANNs) were introduced as a viable candidate for advanced non-linear equalizers, as they outperform traditional methods. However, they are computationally complex and therefore power hungry. Spiking neural networks (SNNs) started to gain attention as an energy-efficient alternative to ANNs. Recent works proved that they can outperform ANNs at this task. In this work, we explore the design space of an SNN-based decision-feedback equalizer (DFE) to reduce its computational complexity for an efficient implementation on field programmable gate array (FPGA). Our Results prove that it achieves higher communication performance than ANN-based DFE at roughly the same throughput and at 25X higher energy efficiency. |
| title | Efficient FPGA Implementation of an Optimized SNN-based DFE for Optical Communications |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2409.08698 |