New Trends in Photonic Switching and Optical Network Architecture for Data Centre and Computing Systems

AI/ML for data centres and data centres for AI/ML are defining new trends in cloud computing. Disaggregated heterogeneous reconfigurable computing systems realized by photonic interconnects and photonic switching expect greatly enhanced throughput and energy-efficiency for AI/ML workloads, especially when aided by an AI/ML control plane

Low-Mass Planar Photonic Imaging Sensor

Continuing on the successful progress of NIAC Phase I, this report summarizes the technical progress achieved under NIAC Phase II during the performance period September 19, 2014-June 18, 2017. During this period, the research team has made the following accomplishments: designed and layout a silica photonic integrated circuit (PIC) as a two baseline interferometric imager; constructed an experiment to utilize the two baselines for complex visibility measurement on a point source and a variable width slit; analyzed and studied the testbed results. (in collaboration with Lockheed Martin); designed and layout Si3N4 PICs for the low-resolution and high-resolution SPIDER telescope; fabricated the multi-layer Si3N4 PIC for low and high resolution SPIDER telescope; characterize the optical throughput and heater response for Si3N4 PIC for low and high resolution SPIDER telescopes; carried out imaging experiments using the Si3N4 PIC low-resolution version (in collaboration with Lockheed Martin); investigated signal-to-noise (SNR) ratio of SPIDER imager compared to the conventional panchromatic imager (in collaboration with Lockheed Martin); fulfilled the SNR simulation upon SPIDER imager (in collaboration with Lockheed Martin)

Low-Mass Planar Photonic Imaging Sensor

Continuing on the successful progress of NIAC (NASA Innovative Advanced Concepts) Phase I, this report summarizes the technical progress achieved under NIAC Phase II during the performance period September 19, 2014 to June 18, 2017. During this period, the research team has made the following accomplishments: designed and layout a silica photonic integrated circuit (PIC) as a two baselineinterferometric imager; constructed an experiment to utilize the two baselines for complex visibility measurementon a point source and a variable width slit; analyzed and studied the testbed results (in collaboration with Lockheed Martin); designed and layout Si3N4 PICs for the low-resolution and high-resolution SPIDER (Segmented Planar Imaging Detector for Electro-Optical Reconnaissance) telescope; fabricated the multi-layer Si3N4 PIC for low and high resolution SPIDER telescope; characterized the optical throughput and heater response for Si3N4 PIC for low and highresolution SPIDER telescopes; carried out imaging experiments using the Si3N4 PIC low-resolution version (in collaboration with Lockheed Martin); investigated signal-to-noise (SNR) ratio of SPIDER imager compared to the conventional panchromatic imager (in collaboration with Lockheed Martin); fulfilled the SNR simulation upon SPIDER imager (in collaboration with Lockheed Martin)

Which can Accelerate Distributed Machine Learning Faster: Hybrid Optical/Electrical or Optical Reconfigurable DCN?

We run various distributed machine learning (DML) architectures in a hybrid optical/electrical DCN and an optical DCN based on Hyper-FleX-LION. Experimental results show that Hyper-FleX-LION gains faster DML acceleration and improves acceleration ratio by up to 22.3%

Machine-Learning-Aided Dynamic Reconfiguration in Optical DC/HPC Networks (Invited)

The high bandwidth and low latency requirements of modern computing applications with their dynamic and nonuniform traffic patterns impose severe challenges to current data center (DC) and high performance computing (HPC) networks. Therefore, we present a dynamic network reconfiguration mechanism that could satisfy the time-varying applications' demands in an optical DC/HPC network. We propose a direct and an indirect topology extraction methods based on a machine learning-Aided traffic prediction approach under multi-Application scenario. The traffic prediction for topology extraction and bandwidth reconfiguration (PredicTER) method could lead to frequent topology and bandwidth reconfiguration. In contrast, the indirect approach, namely traffic prediction with clustering for topology extraction and bandwidth reconfiguration (PrediCLUSTER), utilizes an unsupervised learning-based clustering model to first associate the predicted traffic to one of possible traffic clusters, and then extracts a common topology for the cluster. This restricts the reconfigured topology set to the number of traffic clusters. Our simulation results show that the time-Average of mean packet latencies (and total dropped packets) over 60 seconds of timevarying traffic under the PredicTER, PrediCLUSTER and a static topology are 37.7μs,41.2μs, and 50.2μs (and 37,967, 12,305, and 36,836), respectively. Overall, the PredicTER (and PrediCLUSTER) method(s) can improve the end-To-end packet latency by 24.9% (and 17.8%), and the packet loss rate by-3.1% (and 66.6%), as compared to the static flat Hyper-X-like topology

Collaborative learning in multi-domain optical networks

This paper presents a collaborative learning framework for multi-domain optical networks to enable cognitive end-to-end networking while guaranteeing the autonomy of each administrative domain

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

First Demonstration of Monolithic Silicon Photonic Integrated Circuit 32×32 Thin-CLOS AWGR for All-to-All Interconnections

We designed, fabricated, and demonstrated the first monolithic silicon photonic Thin-CLOS AWGR. The fabricated Thin-CLOS has 32 ports and four 16-port silicon nitride AWGRs integrated by compact multilayer waveguide routing. Experimental results show 4 dB insertion loss and-20 dB crosstalk

Analysis of the Hardware Imprecisions for Scalable and Compact Photonic Tensorized Neural Networks

We simulated tensor-train decomposed neural networks realized by Mach-Zehnder interferometer-based scalable photonic neuromorphic devices. The simulation results demonstrate that under practical hardware imprecisions, the TT-decomposed neural networks can achieve >90% test accuracy with 33.6× fewer MZIs than conventional photonic neural network implementations

Programmable Integrated Photonics for Topological Hamiltonians

A variety of topological Hamiltonians have been demonstrated in photonic platforms, leading to fundamental discoveries and enhanced robustness in applications such as lasing, sensing, and quantum technologies. To date, each topological photonic platform implements a specific type of Hamiltonian with inexistent or limited reconfigurability. Here, we propose and demonstrate different topological models by using the same reprogrammable integrated photonics platform, consisting of a hexagonal mesh of silicon Mach-Zehnder interferometers with phase-shifters. We specifically demonstrate a one-dimensional Su-Schrieffer-Heeger Hamiltonian supporting a localized topological edge mode and a higher-order topological insulator based on a two-dimensional breathing Kagome Hamiltonian with three corner states. These results highlight a nearly universal platform for topological models that may fast-track research progress toward applications of topological photonics and other coupled systems