Photonic Neural Networks and Optics-informed Deep Learning Fundamentals

• Main Authors: Tsakyridis, A, Moralis-Pegios, M, Giamougiannis, G, Kirtas, M, Passalis, N, Tefas, A, Pleros, N
• Format: Journal Article
• Language: English
• Published: 22-11-2023
• Subjects: Computer Science - Emerging Technologies; Physics - Optics
• DOI: 10.48550/arxiv.2312.00037

The recent explosive compute growth, mainly fueled by the boost of AI and
DNNs, is currently instigating the demand for a novel computing paradigm that
can overcome the insurmountable barriers imposed by conventional electronic
computing architectures. PNNs implemented on silicon integration platforms
stand out as a promising candidate to endow NN hardware, offering the potential
for energy efficient and ultra-fast computations through the utilization of the
unique primitives of photonics i.e. energy efficiency, THz bandwidth 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 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 tutorial, we,
initially, review the principles of DNNs along with their fundamental building
blocks, analyzing also the key mathematical operations needed for their
computation in a photonic hardware. Then, we investigate, through an intuitive
mathematical analysis, the interdependence of bit precision and energy
efficiency in analog photonic circuitry, discussing the opportunities and
challenges of PNNs. Followingly, a performance overview of PNN architectures,
weight technologies and activation functions is presented, summarizing their
impact in speed, scalability and power consumption. 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.