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.

翻译：光学脉冲神经网络（SNN）的研究主要集中于脉冲器件、可激发激光器网络或大型架构的数值建模，往往忽视了有限光功率、串扰和占地面积等关键约束。我们提出了SEPhIA，一种强调实现可行性和实际可扩展性的光电集成多瓦片SNN架构。SEPhIA利用微环谐振调制器（MRM）和多波长光源，实现了每个脉冲神经元平均低于一个激光器的有效效率。我们在器件和架构两个层面验证了SEPhIA：通过时域协同仿真可激发的CMOS-MRR耦合电路，以及构建一个物理感知、可训练的光电SNN模型。两种方法均采用实验测得的器件参数。该多层光电SNN在四类脉冲编码数据集上实现了超过90%的分类准确率，与软件模型性能相当接近。一项设计空间研究进一步量化了在信噪比受限条件下，光子器件参数如何影响SNN性能。SEPhIA为神经形态光子计算提供了一个可扩展、表达能力强且物理基础坚实的解决方案，能够处理脉冲编码任务。