The Role of Photonics in Neuromorphic Computing for Augmenting the von Neumann Paradigm

Abstract

The rapid expansion of Big Data and Artificial Intelligence has exposed fundamental inefficiencies in conventional von Neumann computing architectures, thus necessitating a search for alternative solutions. Neuromorphic computing, inspired by biological neural systems, has emerged as a promising route toward faster and more energy‐efficient information processing approaches. Optical neuromorphic computing exploits light as a signal modality for stimulation, data delivery, information processing, and signal readout, offering intrinsic advantages in speed, parallelism, and power consumption. This review outlines the core principles of neuromorphic computing and examines recent advances in its optical implementations, with a particular focus on optical artificial synapses, photonic layers integrated into hybrid photonic‐digital architectures, and optical neural networks. These emerging approaches define a coherent framework for advancing computing systems that offer clear advantages over purely von Neumann systems.