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

Photonic spike processing: ultrafast laser neurons and an integrated photonic network

The marriage of two vibrant fields---photonics and neuromorphic processing---is fundamentally enabled by the strong analogies within the underlying physics between the dynamics of biological neurons and lasers, both of which can be understood within the framework of nonlinear dynamical systems theory. Whereas neuromorphic engineering exploits the biophysics of neuronal computation algorithms to provide a wide range of computing and signal processing applications, photonics offer an alternative approach to neuromorphic systems by exploiting the high speed, high bandwidth, and low crosstalk available to photonic interconnects which potentially grants the capacity for complex, ultrafast categorization and decision-making. Here we highlight some recent progress on this exciting field.Comment: 11 pages, 8 figure

Access and metro network convergence for flexible end-to-end network design

This paper reports on the architectural, protocol, physical layer, and integrated testbed demonstrations carried out by the DISCUS FP7 consortium in the area of access - metro network convergence. Our architecture modeling results show the vast potential for cost and power savings that node consolidation can bring. The architecture, however, also recognizes the limits of long-reach transmission for low-latency 5G services and proposes ways to address such shortcomings in future projects. The testbed results, which have been conducted end-to-end, across access - metro and core, and have targeted all the layers of the network from the application down to the physical layer, show the practical feasibility of the concepts proposed in the project

Architectural Exploration and Design Methodologies of Photonic Interconnection Networks

Photonic technology is becoming an increasingly attractive solution to the problems facing today's electronic chip-scale interconnection networks. Recent progress in silicon photonics research has enabled the demonstration of all the necessary optical building blocks for creating extremely high-bandwidth density and energy-efficient links for on- and off-chip communications. From the feasibility and architecture perspective however, photonics represents a dramatic paradigm shift from traditional electronic network designs due to fundamental differences in how electronics and photonics function and behave. As a result of these differences, new modeling and analysis methods must be employed in order to properly realize a functional photonic chip-scale interconnect design. In this work, we present a methodology for characterizing and modeling fundamental photonic building blocks which can subsequently be combined to form full photonic network architectures. We also describe a set of tools which can be utilized to assess the physical-layer and system-level performance properties of a photonic network. The models and tools are integrated in a novel open-source design and simulation environment called PhoenixSim. Next, we leverage PhoenixSim for the study of chip-scale photonic networks. We examine several photonic networks through the synergistic study of both physical-layer metrics and system-level metrics. This holistic analysis method enables us to provide deeper insight into architecture scalability since it considers insertion loss, crosstalk, and power dissipation. In addition to these novel physical-layer metrics, traditional system-level metrics of bandwidth and latency are also obtained. Lastly, we propose a novel routing architecture known as wavelength-selective spatial routing. This routing architecture is analogous to electronic virtual channels since it enables the transmission of multiple logical optical channels through a single physical plane (i.e. the waveguides). The available wavelength channels are partitioned into separate groups, and each group is routed independently in the network. Each partition is spectrally multiplexed, as opposed to temporally multiplexed in the electronic case. The wavelength-selective spatial routing technique benefits network designers by provider lower contention and increased path diversity

Reliability-aware multi-segmented bus architecture for photonic networks-on-chip

Network-on-chip (NoC) has emerged as an enabling platform for connecting hundreds of cores on a single chip, allowing for a structured, scalable system when compared to traditional on-chip buses. However, the multi-hop wireline paths in traditional NoCs result in high latency and energy dissipation causing an overall degradation in performance, especially for increasing system size. To alleviate this problem a few radically different interconnect technologies are envisioned. One such method of interconnecting different cores in NoCs is photonic interconnects. Photonic NoCs are on-chip communications networks in which information is transmitted in the form of optical signals. Photonic interconnection is one of the leading examples of emerging technology for on-chip interconnects. Existing innovative photonic NoC architectures have improved performance and reduced energy dissipation. Most architectures use Wavelength Division Multiplexing (WDM) on the photonic waveguides to increase the data bandwidth. However they have issues relating to reliability, such as waveguide losses and adjacent channel crosstalk. These phenomena could have a crippling effect on a system, and most current architectures do not address these effects. A newly proposed topology, known as the Multiple-Segmented Bus topology, or MSB, has shown promise for solving, or at least reducing, many of the problems plaguing the design of photonic networks using a modification of a folded torus to transmit different wavelength signals simultaneously. The MSB segments the waveguides into smaller parts to limit the waveguide losses. The formal performance evaluation of this proposed architecture has not been completed. This thesis will analyze the performance of such a network when implemented as a NoC in terms of data bandwidth, energy dissipation, latency, and reliability. By analyzing and comparing performance, energy dissipations, and reliability, the MSB-based photonic NoC (MSB-PNoC) can be compared to other state-of-the-art photonic NoCs to determine the feasibility of this topology for future network-on-chip designs

DSENT - A Tool Connecting Emerging Photonics with Electronics for Opto-Electronic Networks-on-Chip Modeling

With the advent of many-core chips that place substantial demand on the NoC, photonics has been investigated as a promising alternative to electrical NoCs. While numerous opto-electronic NoCs have been proposed, their evaluations tend to be based on fixed numbers for both photonic and electrical components, making it difficult to co-optimize. Through our own forays into opto-electronic NoC design, we observe that photonics and electronics are very much intertwined, reflecting a strong need for a NoC modeling tool that accurately models parameterized electronic and photonic components within a unified framework, capturing their interactions faithfully. In this paper, we present a tool, DSENT, for design space exploration of electrical and opto-electrical networks. We form a framework that constructs basic NoC building blocks from electrical and photonic technology parameters. To demonstrate potential use cases, we perform a network case study illustrating data-rate tradeoffs, a comparison with scaled electrical technology, and sensitivity to photonics parameters