Neuromorphic Wireless Split Computing with Resonate-and-Fire Neurons
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911021208174592 |
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| author | Wu, Dengyu Chen, Jiechen Poor, H. Vincent Rajendran, Bipin Simeone, Osvaldo |
| author_facet | Wu, Dengyu Chen, Jiechen Poor, H. Vincent Rajendran, Bipin Simeone, Osvaldo |
| contents | Neuromorphic computing offers an energy-efficient alternative to conventional deep learning accelerators for real-time time-series processing. However, many edge applications, such as wireless sensing and audio recognition, generate streaming signals with rich spectral features that are not effectively captured by conventional leaky integrate-and-fire (LIF) spiking neurons. This paper investigates a wireless split computing architecture that employs resonate-and-fire (RF) neurons with oscillatory dynamics to process time-domain signals directly, eliminating the need for costly spectral pre-processing. By resonating at tunable frequencies, RF neurons extract time-localized spectral features while maintaining low spiking activity. This temporal sparsity translates into significant savings in both computation and transmission energy. Assuming an OFDM-based analog wireless interface for spike transmission, we present a complete system design and evaluate its performance on audio classification and modulation classification tasks. Experimental results show that the proposed RF-SNN architecture achieves comparable accuracy to conventional LIF-SNNs and ANNs, while substantially reducing spike rates and total energy consumption during inference and communication. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_20015 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Neuromorphic Wireless Split Computing with Resonate-and-Fire Neurons Wu, Dengyu Chen, Jiechen Poor, H. Vincent Rajendran, Bipin Simeone, Osvaldo Machine Learning Information Theory Neural and Evolutionary Computing Neuromorphic computing offers an energy-efficient alternative to conventional deep learning accelerators for real-time time-series processing. However, many edge applications, such as wireless sensing and audio recognition, generate streaming signals with rich spectral features that are not effectively captured by conventional leaky integrate-and-fire (LIF) spiking neurons. This paper investigates a wireless split computing architecture that employs resonate-and-fire (RF) neurons with oscillatory dynamics to process time-domain signals directly, eliminating the need for costly spectral pre-processing. By resonating at tunable frequencies, RF neurons extract time-localized spectral features while maintaining low spiking activity. This temporal sparsity translates into significant savings in both computation and transmission energy. Assuming an OFDM-based analog wireless interface for spike transmission, we present a complete system design and evaluate its performance on audio classification and modulation classification tasks. Experimental results show that the proposed RF-SNN architecture achieves comparable accuracy to conventional LIF-SNNs and ANNs, while substantially reducing spike rates and total energy consumption during inference and communication. |
| title | Neuromorphic Wireless Split Computing with Resonate-and-Fire Neurons |
| topic | Machine Learning Information Theory Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2506.20015 |