SEPhIA: <1 laser/neuron Spiking Electro-Photonic Integrated Multi-Tiled Architecture for Scalable Optical Neuromorphic Computing
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911199466094592 |
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| author | Hejda, Matěj Natarajan, Aishwarya Hong, Chaerin On, Mehmet Berkay de Thun, Sébastien d'Herbais Beausoleil, Raymond G. Van Vaerenbergh, Thomas |
| author_facet | Hejda, Matěj Natarajan, Aishwarya Hong, Chaerin On, Mehmet Berkay de Thun, Sébastien d'Herbais Beausoleil, Raymond G. Van Vaerenbergh, Thomas |
| contents | Research into optical spiking neural networks (SNNs) has primarily focused on spiking devices, networks of excitable lasers or numerical modelling of large architectures, often overlooking key constraints such as limited optical power, crosstalk and footprint. We introduce SEPhIA, a photonic-electronic, multi-tiled SNN architecture emphasizing implementation feasibility and realistic scaling. SEPhIA leverages microring resonator modulators (MRMs) and multi-wavelength sources to achieve effective sub-one-laser-per-spiking neuron efficiency. We validate SEPhIA at both device and architecture levels by time-domain co-simulating excitable CMOS-MRR coupled circuits and by devising a physics-aware, trainable optoelectronic SNN model, with both approaches utilizing experimentally derived device parameters. The multi-layer optoelectronic SNN achieves classification accuracies over 90% on a four-class spike-encoded dataset, closely comparable to software models. A design space study further quantifies how photonic device parameters impact SNN performance under constrained signal-to-noise conditions. SEPhIA offers a scalable, expressive, physically grounded solution for neuromorphic photonic computing, capable of addressing spike-encoded tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_07427 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SEPhIA: <1 laser/neuron Spiking Electro-Photonic Integrated Multi-Tiled Architecture for Scalable Optical Neuromorphic Computing Hejda, Matěj Natarajan, Aishwarya Hong, Chaerin On, Mehmet Berkay de Thun, Sébastien d'Herbais Beausoleil, Raymond G. Van Vaerenbergh, Thomas Emerging Technologies Neural and Evolutionary Computing Optics Research into optical spiking neural networks (SNNs) has primarily focused on spiking devices, networks of excitable lasers or numerical modelling of large architectures, often overlooking key constraints such as limited optical power, crosstalk and footprint. We introduce SEPhIA, a photonic-electronic, multi-tiled SNN architecture emphasizing implementation feasibility and realistic scaling. SEPhIA leverages microring resonator modulators (MRMs) and multi-wavelength sources to achieve effective sub-one-laser-per-spiking neuron efficiency. We validate SEPhIA at both device and architecture levels by time-domain co-simulating excitable CMOS-MRR coupled circuits and by devising a physics-aware, trainable optoelectronic SNN model, with both approaches utilizing experimentally derived device parameters. The multi-layer optoelectronic SNN achieves classification accuracies over 90% on a four-class spike-encoded dataset, closely comparable to software models. A design space study further quantifies how photonic device parameters impact SNN performance under constrained signal-to-noise conditions. SEPhIA offers a scalable, expressive, physically grounded solution for neuromorphic photonic computing, capable of addressing spike-encoded tasks. |
| title | SEPhIA: <1 laser/neuron Spiking Electro-Photonic Integrated Multi-Tiled Architecture for Scalable Optical Neuromorphic Computing |
| topic | Emerging Technologies Neural and Evolutionary Computing Optics |
| url | https://arxiv.org/abs/2510.07427 |