Optical Neural Network

Optical neural networks (ONNs) leverage the inherent parallelism of light to perform neural network computations, aiming for faster and more energy-efficient machine learning than electronic counterparts. Current research focuses on improving training efficiency through novel algorithms like forward-forward training and hybrid approaches combining optical and digital computation, as well as developing specialized architectures such as diffraction neural networks and those employing multi-operand optical neurons to enhance performance and scalability. These advancements address limitations in precision, noise resilience, and hardware efficiency, with potential applications ranging from image recognition and scientific computing to solving partial differential equations.