Optical neural networks have great potential to process information efficiently with the speed of light. Compared electronic competitors, multiple tasks been successfully demonstrated in such computing systems several orders magnitude improvement energy consumption. However, current training strategies, as a critical step for bringing optical into reality, are still at an early stage. The high ...

With the continuous miniaturization of conventional integrated circuits, obstacles such as excessive cost, increased resistance to electronic motion, and energy consumption are gradually slowing down development electrical computing constraining application deep learning. Optical neuromorphic presents various opportunities challenges compared with realm electronics. Algorithms running on optica...

Optical neural networks (ONNs), implemented on an array of cascaded Mach–Zehnder interferometers (MZIs), have recently been proposed as a possible replacement for conventional deep learning hardware. They potentially offer higher energy efficiency and computational speed when compared to their electronic counterparts. By utilizing tunable phase shifters, one can adjust the output each MZI enabl...

In the past years, telecommunications networks have seen an important evolution with the advances in optical technologies and the explosive growth of the Internet. Several optical systems allow a very large transport capacity, and data tra c has dramatically increased. Telecommunications networks are now moving towards a model of high-speed routers interconnected by intelligent optical core net...

In this paper the transient control using effect of steady and relaxation oscillation in optical fiber based on neural networks and hardware implementation is described. The inherent optical feedback by the backscattered Stokes wave in optical fiber leads to instabilities in the form of optical chaos. The controlling of chaos induced transient instability in Brillouin-active fiber based smart s...