Research in photonic computing has flourished due to the proliferation of optoelectronic components on photonic integration platforms. Photonic integrated circuits have enabled ultrafast artificial neural networks, providing a framework for a new class of information processing machines. Algorithms running on such hardware have the potential to address the growing demand for machine learning and artificial intelligence in areas such as medical diagnosis, telecommunications, and high-performance and scientific computing. In parallel, the development of neuromorphic electronics has highlighted challenges in that domain, particularly related to processor latency. Neuromorphic photonics offers sub-nanosecond latencies, providing a complementary opportunity to extend the domain of artificial intelligence. Here, we review recent advances in integrated photonic neuromorphic systems, discuss current and future challenges, and outline the advances in science and technology needed to meet those challenges. Photonics offers an attractive platform for implementing neuromorphic computing due to its low latency, multiplexing capabilities and integrated on-chip technology.

Photonics for artificial intelligence and neuromorphic computing

Research in photonic computing has flourished due to the proliferation of optoelectronic components on photonic integration platforms. Photonic integrated circuits have enabled ultrafast artificial neural networks, providing a framework for a new class of information processing machines. Algorithms running on such hardware have the potential to address the growing demand for machine learning and artificial intelligence, in areas such as medical diagnosis, telecommunications, and high-performance and scientific computing. In parallel, the development of neuromorphic electronics has highlighted challenges in that domain, in particular, related to processor latency. Neuromorphic photonics offers sub-nanosecond latencies, providing a complementary opportunity to extend the domain of artificial intelligence. Here, we review recent advances in integrated photonic neuromorphic systems, discuss current and future challenges, and outline the advances in science and technology needed to meet those challenges.

Optical performance monitoring (OPM) is the estimation and acquisition of different physical parameters of transmitted signals and various components of an optical network. OPM functionalities are indispensable in ensuring robust network operation and plays a key role in enabling flexibility and improve overall network efficiency. We review the development of various OPM techniques for direct-detection systems and digital coherent systems and discuss future OPM challenges in flexible and elastic optical networks.

Optical Performance Monitoring: A Review of Current and Future Technologies

Machine learning (ML) has disrupted a wide range of science and engineering disciplines in recent years. ML applications in optical communications and networking are also gaining more attention, particularly in the areas of nonlinear transmission systems, optical performance monitoring, and cross-layer network optimizations for software-defined networks. However, the extent to which ML techniques can benefit optical communications and networking is not clear and this is partly due to an insufficient understanding of the nature of ML concepts. This paper aims to describe the mathematical foundations of basic ML techniques from communication theory and signal processing perspectives, which in turn will shed light on the types of problems in optical communications and networking that naturally warrant ML use. This will be followed by an overview of ongoing ML research in optical communications and networking with a focus on physical layer issues.

An Optical Communication's Perspective on Machine Learning and Its Applications

Research on space-division multiplexing (SDM) came to prominence in early 2010 being primarily proposed as a means of multiplying the information-carrying capacity of optical fibers at the same time as increasing efficiency through resource sharing. Proposed SDM transmission systems range from parallel single-mode fibers with shared amplifier pump lasers to the full spatial integration of transceiver hardware, signal processing, and amplification around a fiber with over 100 spatial channels comprising multiple cores each carrying multiple modes. In this paper, we review progress in SDM research. We first outline the main classifications and features of novel SDM fibers such as multicore fibers (MCFs), multimode fibers, few-mode MCFs, and coupled-core MCFs. We review research achievements of each fiber type before discussing digital-signal processing, amplifier technology, and milestones of transmission and networking demonstrations. Finally, we draw comparisons between fiber types before discussing the current trends and speculate on future developments and applications beyond optical data transmission.

Space-division multiplexing for optical fiber communications

Fiber nonlinearity is one of the major limitations to the achievable capacity in long distance fiber optic transmission systems. Nonlinear impairments are determined by the signal pattern and the transmission system parameters. Deterministic algorithms based on approximating the nonlinear Schrodinger equation through digital back propagation, or a single step approach based on perturbation methods have been demonstrated, however, their implementation demands excessive signal processing resources, and accurate knowledge of the transmission system. A completely different approach uses machine learning algorithms to learn from the received data itself to figure out the nonlinear impairment. In this work, a single-step, system agnostic nonlinearity compensation algorithm based on a neural network is proposed to pre-distort symbols at transmitter side to demonstrate ~0.6 dB Q improvement after 2800 km standard single-mode fiber transmission using 32 Gbaud signal. Without prior knowledge of the transmission system, the neural network tensor weights are constructed from training data thanks to the intra-channel cross-phase modulation and intra-channel four-wave mixing triplets used as input features. Long-distance fiber communications still face many fundamental challenges in capacity due to nonlinearities. The authors develop a neural-network based tool to compensate nonlinearities, without prior knowledge of the transmission link, with low complexity.

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Field and lab experimental demonstration of nonlinear impairment compensation using neural networks

Mega data centers and their interconnection networks have drawn great attention in recent years because of the rapid public adoption of cloud-based services. The unprecedented amount of data that needs to be communicated between data centers imposes new requirements and challenges to inter-data-center optical networks. In this article, we discuss the traffic growth trends and capacity demands of Google?s inter-data-center network, and how they drive the network architectures and technologies to scale capacities and operational ease on existing fiber plants. We extensively review recent research findings and emerging technologies, such as digital coherent detection and the flexgrid dense wavelength-division multiplexed channel plan, and propose practical implementations, such as C+L-band transmission, packet and optical layer integration, and a software-defined networking enabled network architecture for both capacity and operational scaling. In addition, we point out a few critical areas that require more attention and research to improve efficiency and flexibility of an inter-data-center optical network: optical regeneration, data rate mismatch between Ethernet and optical transport, and real-time optical performance monitoring.

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The prospect of inter-data-center optical networks

C+L open line systems represent a cost-effective way to scale backbone network capacity. In this article, we review challenges and opportunities for C+L line systems stemming from Google's experience in designing, deploying, and operating a global C+L open optical network. We discuss business, operational, and technical aspects of C+L systems, and describe best practices for designing C+L links. Finally, we compare C and C+L systems, showing how the latter not only conceal capacity penalties but can even increase, depending on the deployed fiber types, the total system capacity with respect to two parallel C-band only systems.

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Opportunities and Challenges of C+L Transmission Systems

We report on the evolution of the longest segment of FASTER cable at 11,017 km, with 8QAM transponders at 4b/s/Hz spectral efficiency (SE) in service. With offline testing, 6 b/s/Hz is further demonstrated using probabilistically shaped 64QAM, and a novel, low complexity nonlinearity compensation technique based on generating a black-box model of the transmission by training an artificial neural network, resulting in the largest SE-distance product 66,102 b/s/Hz-km over live-traffic carrying cable.

Evolution from 8QAM live traffic to PS 64-QAM with Neural-Network Based Nonlinearity Compensation on 11000 km Open Subsea Cable

We report on two techniques for the realization of expanded-mode laser arrays with a single epitaxial growth step and conventional fabrication techniques. Laser arrays with integrated adiabatic-mode expanders (AME) based on a tapered active region and an underlying passive coupling waveguide are demonstrated at the 1.55-/spl mu/m wavelength. These lasers butt couple to standard cleaved single-mode fibers (SMF's) with a loss of only 3.6 dB. This coupling efficiency compares with a theoretical calculation of 3.2 dB. We also propose a novel realization of a laser with an integrated-mode expander based on resonant coupling between a tapered active waveguide and an underlying coupling waveguide. Three-dimensional (3-D) beam propagation method (BPM) results are presented which show that compact, efficient mode expanders with a mode transformation loss of only 0.36 dB can be realized using this method. Butt-coupling efficiencies of 2.6 dB are possible to standard cleaved single-mode fibers.

Vijay Vusirikala

Papers

Field and lab experimental demonstration of nonlinear impairment compensation using neural networks

The prospect of inter-data-center optical networks

Opportunities and Challenges of C+L Transmission Systems

Evolution from 8QAM live traffic to PS 64-QAM with Neural-Network Based Nonlinearity Compensation on 11000 km Open Subsea Cable

1.55-/spl mu/m InGaAsP-InP laser arrays with integrated-mode expanders fabricated using a single epitaxial growth