Neuromorphic Photonics With Coherent Linear Neurons Using Dual-IQ Modulation Cells

Abstract

Neuromorphic photonics aims to transfer the high-bandwidth and low-energy credentials of optics into neuromorphic computing architectures. In this effort, photonic neurons are trying to combine the optical interconnect segments with optics that can realize all critical constituent neuromorphic functions, including the linear neuron stage and the activation function. However, aligning this new platform with well-established neural network training models in order to allow for the synergy of the photonic hardware with the best-in-class training algorithms, the following requirements should apply: i) the linear photonic neuron has to be able to handle both positive and negative weight values, ii) the activation function has to closely follow the widely used mathematical activation functions that have already shown an enormous performance in demonstrated neural networks so far. Herein, we demonstrate a coherent linear neuron architecture that relies on a dual-IQ modulation cell as its basic neuron element, introducing distinct optical elements for weight amplitude and weight sign representation and exploiting binary optical carrier phase-encoding for positive/negative number representation. We present experimental results of a typical IQ modulator performing as an elementary two-input linear neuron cell and successfully implementing all-optical linear algebraic operations with 104-ps long optical pulses. We also provide the theoretical proof and formulation of how to extend a dual-IQ modulation cell into a complete $N$ -input coherent linear neuron stage that requires only a single-wavelength optical input and avoids the resource-consuming Wavelength Division Multiplexing (WDM) weighting schemes. An 8-input coherent linear neuron is then combined with an experimentally validated optical sigmoid activation function into a physical layer simulation environment, with respective training and physical layer simulation results for the MNIST dataset revealing an average accuracy of 97.24% and 94.37%, respectively.