Optics-informed neural networks towards accelerating linear operations

The recent explosive compute growth, mainly fueled by the boost of artificial intelligence (AI) and deep neural networks (DNNs), is currently instigating the demand for a novel computing paradigm that can overcome the insurmountable barriers imposed by conventional electronic computing architectures. Photonic neural networks (PNNs) implemented on silicon photonic integration platforms stand out as a promising candidate to endow neural network (NN) hardware, offering the potential for energy efficient and ultra-fast computations through the utilization of the unique primitives of light i.e. THz bandwidth, low-power and low-latency. Thus far, several demonstrations have revealed the huge potential of PNNs in performing both linear and non-linear NN operations at unparalleled speed and energy consumption metrics. Transforming this potential into a tangible reality for Deep Learning (DL) applications requires, however, a deep understanding of the basic PNN principles, requirements and challenges across all constituent architectural, technological and training aspects. In this paper we review the state-of-the-art photonic linear processors and project their challenges and solutions for future photonic-assisted machine learning engines. Additionally, recent experimental results using SiGe EAMs in a Xbar layout are presented, validating light's credentials to perform ultra-fast linear operations with unparalleled accuracy. Finally, we provide an holistic overview of the optics-informed NN training framework that incorporates the physical properties of photonic building blocks into the training process in order to improve the NN classification accuracy and effectively elevate neuromorphic photonic hardware into high-performance DL computational settings.