Training a Multi-Layer Photonic Spiking Neural Network With Modified Supervised Learning Algorithm Based on Photonic STDP

Abstract

We propose a framework for hardware architecture and learning algorithm co-design of multi-layer photonic spiking neural network (SNN). The vertical-cavity surface-emitting laser with an embedded saturable absorber (VCSEL-SA) which contains two polarization-resolved modes is employed as a spiking neuron. The connection between two identical polarization modes is considered as the excitatory synapse, whereas the connection between two orthogonal polarization modes is regarded as the inhibitory synapse. The physical model of the photonic spiking neuron is derived based on the combination of spin-flip model and Yamada model. The photonic spike timing dependent plasticity (STDP) is applied to design a hardware-friendly biologically plausible supervised learning algorithm for a multi-layer photonic SNN. Thanks to the polarization mode competition effect in the VCSEL-SA, the proposed neuromorphic network is capable of solving the classical XOR problem. The effect of physical parameters of photonic neuron on the training convergence is also considered. We further extend the multi-layer photonic SNN to realize other logic learning tasks. To the best of our knowledge, such a modified supervised learning algorithm dedicated for a multi-layer photonic SNN has not yet been reported, which is interesting for spiking learning of neuromorphic photonics.