An Optical Neural Network Architecture based on Highly Parallelized WDMMultiplierAccumulator Future applications such as anomaly detection in a network and autonomous driving require extremely low, submicrosecond latency processing in pattern classification. Towards the realization of such an ultrafast inference processing, this paper proposes an optical neural network architecture which can classify anomaly patterns at subnanosecond latency. The architecture fully exploits optical parallelism of lights using wavelength division multiplexing (WDM) in vectormatrix multiplication. It also exploits a linear optics with passive nanophotonic devices such as microring resonators, optical combiners, and passive couplers, which make it possible to construct low power and ultralow latency optical neural networks. Optoelectronic circuit simulation using optical circuit implementation of multilayer perceptron (MLP) demonstrates subnanosecond processing of optical neural network.

Machine learning for intelligent optical networks: A comprehensive survey Abstract   With the rapid development of Internet and communication systems, both in the aspect of services and technologies, communication networks have been suffering increasing complexity. It is imperative to improve intelligence in communication networks, and several aspects have been incorporating with Artificial Intelligence (AI) and Machine Learning (ML). The optical network, which plays an important role both in core and access network in communication networks, also faces great challenges of system complexity and the requirement of manual operations. To overcome the current limitations and address the issues of future optical networks, it is essential to deploy more intelligence capability to enable autonomous and flexible network operations. ML techniques are proved to have superiority on solving complex problems, and thus recently, ML techniques have been used for many optical network applications.  In this paper, a detailed survey of existing applications of ML for intelligent optical networks is presented. The applications of ML are classified in terms of their use cases, which are categorized into optical network control and resource management, and optical network monitoring and survivability. These applications are analyzed and compared according to the used ML techniques. Besides, a tutorial for ML applications is provided from the aspects of the introduction of common ML algorithms, paradigms of ML, and motivations of applying ML. Lastly, challenges and possible solutions of ML application in intelligent optical networks are also discussed, which intends to inspire future innovations in leveraging ML to build intelligent optical networks.