Exploiting AWG Free Spectral Range Periodicity in Distributed Multicast Architectures

Modular optical switch architectures combining wavelength routing based on arrayed waveguide grating (AWG) devices and multicasting based on star couplers hold promise for flexibly addressing the exponentially growing traffic demands in a cost- and power-efficient fashion. In a default switching scenario, an input port of the AWG is connected to an output port via a single wavelength. This can severely limit the capacity between broadcast domains, resulting in interdomain traffic switching bottlenecks. In this paper, we examine the possibility of resolving capacity bottlenecks by exploiting multiple AWG free spectral ranges (FSRs), i.e., setting up multiple parallel connections between each pair of broadcast domains. To this end, we introduce a multi-FSR scheduling algorithm for interconnecting broadcast domains by fairly distributing the wavelength resources among them. We develop a general-purpose analytical framework to study the blocking probabilities in a multistage switching scenario and compare our results with Monte Carlo simulations. Our study points to significant improvements with a moderate increase in the number of FSRs. We show that an FSR count beyond four results in diminishing returns. Furthermore, to investigate the trade-offs between the network- and physical-layer effects, we conduct a cross-layer analysis, taking into account pulse amplitude modulation (PAM) and rate-adaptive forward error correction (FEC). We illustrate how the effective bit rate per port increases with an increase in the number of FSRs. %We also look at the advantages of an impairment-aware scheduling strategy in a multi-FSR switching scenario

IPTV deployment - Trigger for advanced network services!

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Principles of Neuromorphic Photonics

In an age overrun with information, the ability to process reams of data has become crucial. The demand for data will continue to grow as smart gadgets multiply and become increasingly integrated into our daily lives. Next-generation industries in artificial intelligence services and high-performance computing are so far supported by microelectronic platforms. These data-intensive enterprises rely on continual improvements in hardware. Their prospects are running up against a stark reality: conventional one-size-fits-all solutions offered by digital electronics can no longer satisfy this need, as Moore's law (exponential hardware scaling), interconnection density, and the von Neumann architecture reach their limits. With its superior speed and reconfigurability, analog photonics can provide some relief to these problems; however, complex applications of analog photonics have remained largely unexplored due to the absence of a robust photonic integration industry. Recently, the landscape for commercially-manufacturable photonic chips has been changing rapidly and now promises to achieve economies of scale previously enjoyed solely by microelectronics. The scientific community has set out to build bridges between the domains of photonic device physics and neural networks, giving rise to the field of \emph{neuromorphic photonics}. This article reviews the recent progress in integrated neuromorphic photonics. We provide an overview of neuromorphic computing, discuss the associated technology (microelectronic and photonic) platforms and compare their metric performance. We discuss photonic neural network approaches and challenges for integrated neuromorphic photonic processors while providing an in-depth description of photonic neurons and a candidate interconnection architecture. We conclude with a future outlook of neuro-inspired photonic processing.Comment: 28 pages, 19 figure

Energy-efficient and Scalable Data Centers with Flexible Bandwidth SiPh All-to-All Fabrics

This paper presents a scalable and energy-efficient flexible-bandwidth optical interconnect architecture for data center networks. The proposed approach leverages silicon photonic reconfigurable all-to-all switch fabrics and a cognitive distributed control plane for optical reconfiguration

Machine-Learning-Aided Bandwidth and Topology Reconfiguration for Optical Data Center Networks

We present an overview of the application of machine learning for traffic engineering and network optimization in optical data center networks. In particular, we discuss the application of supervised and unsupervised learning for bandwidth and topology reconfiguration