SEPhIA shares one multi-wavelength laser across spiking neurons: a two-layer optoelectronic network reaches 91.35% on four-class image classification
HPE Labs proposed and validated SEPhIA, an optoelectronic spiking neural network architecture built on microring modulators and multi-wavelength lasers that lets many spiking neurons share one multi-wavelength light source, pushing the laser-per-neuron ratio below one. Prior photonic spiking work focused mostly on individual spiking devices or all-optical laser networks and rarely addressed optical power, crosstalk and footprint together at the architecture level. Using experimentally derived device parameters, a trainable optoelectronic spiking model reached 91.35% test accuracy on four-class spike-encoded image classification, about one percentage point behind a same-size fully connected spiking baseline at 93.65%.