Performance assessment of optical packet switching system with burst-mode receivers for intra-data centre networks

We investigate the performance of a burst-mode receiver in an optical packet switching system. Experimental results indicate that a preamble of 25.6ns allows error-free operation of 10Gb/s asynchronous switched packets with 8dB dynamic range and 25ns minimum guard-time

Simultaneous optical carrier and radio frequency re-modulation in radio-over-fiber systems employing reflective SOA modulators

We demonstrate an innovative full-duplex radio-over-fibre transmission system employing a reflective SOA to perform simultaneous reusing of the optical carrier and data remodulation, thus avoiding the use of local radiofrequency oscillator at the station sites

In-band label extractor based on Cascaded Si ring resonators enabling 160 Gb/s optical packet switching modules

Photonic integration of optical packet switching modules is crucial to compete with existing electronic switching fabrics in large data center networks. The approach of coding the forwarding packet information in an in-band label enables a spectral-efficient and scalable way of building low-latency large port count modular optical packet switching architecture. We demonstrate the error-free operation of the four in-band label extraction from 160 Gb/s optical data packets based on photonic integrated silicon-on- insulator ring resonators. Four low-loss cascaded ring resonators using the quasi-TM mode are used as narrowband filters to ensure the detection of four optical labels as well as the error-free forwarding of the payload at limited power penalty. Due to the low-loss and less-confined optical quasi-TM mode the resonators can be very narrowband and have low insertion loss. The effect of the bandwidth of the four ring resonators on the quality of the payload is investigated. We show that using four rings with 3dB bandwidth of 21 pm and only an insertion loss of 3 dB, the distortion on the payload is limited (< 1.5 dB power penalty), even when the resonances are placed very close to the packet's central wavelength. We also investigate the optical power requirements for error-free detection of the label as function of their spectral position relative to the center of the payload. The successful in-band positioning of the labels makes this component very scalable in amount of labels

InP photonic integrated multi-layer neural networks:Architecture and performance analysis

We demonstrate the use of a wavelength converter, based on cross-gain modulation in a semiconductor optical amplifier (SOA), as a nonlinear function co-integrated within an all-optical neuron realized with SOA and wavelength-division multiplexing technology. We investigate the impact of fully monolithically integrated linear and nonlinear functions on the all-optical neuron output with respect to the number of synapses/neuron and data rate. Results suggest that the number of inputs can scale up to 64 while guaranteeing a large input power dynamic range of 36 dB with neglectable error introduction. We also investigate the performance of its nonlinear transfer function by tuning the total input power and data rate: The monolithically integrated neuron performs about 10% better in accuracy than the corresponding hybrid device for the same data rate. These all-optical neurons are then used to simulate a 64:64:10 two-layer photonic deep neural network for handwritten digit classification, which shows an 89.5% best-case accuracy at 10 GS/s. Moreover, we analyze the energy consumption for synaptic operation, considering the full end-to-end system, which includes the transceivers, the optical neural network, and the electrical control part. This investigation shows that when the number of synapses/neuron is &gt;18, the energy per operation is &lt;20 pJ (6 times higher than when considering only the optical engine). The computation speed of this two-layer all-optical neural network system is 47 TMAC/s, 2.5 times faster than state-of-the-art graphics processing units, while the energy efficiency is 12 pJ/MAC, 2 times better. This result underlines the importance of scaling photonic integrated neural networks on chip

Data-Driven SOA Parameter Discovery and Optimization Using Bayesian Machine Learning With a Parzen Estimator Surrogate

Semiconductor optical amplifiers (SOAs) are building blocks of several active photonic integrated circuits such as lasers and all optical switches. However, the optimization of SOAs is a computationally expensive task due to the high dimensionality of the problem (i.e Semiconductor optical amplifier (SOA) length, Recombination Parameters, gain coefficient, among others) and the relative long computational time of each simulation run. Furthermore, to accurately simulate optical networks with cascaded SOAs based optical switches one must have access to an accurate model of the device which is not always available. In this work we use a Bayesian Optimization approach based on the single and multi-objective tree-structured Parzen Estimator (TPE) algorithm to find parameters for two wideband models of SOAs operating in different parts of the optical spectrum, the first one in the O-band, and the second one covering the S, C and L-bands. With less than 100 function evaluations and on a limited amount of training (measured) data we are able to obtain general models of both SOAs with a worst average error of 1.12 dB for the gain and -1.86 dB for the optical signal-to-noise ratio (OSNR) in the O-band SOA and a worst average of 0.64 dB for the gain and 0.81 dB for the OSNR in the C-band SOA. We also found that the presented approach outperform common used evolutionary algorithms and Gaussian Processes based Bayesian optimization with regard to the number of required function evaluations, with the TPE obtaining a mean squared error (MSE) of -27 after just 13 trials and the second best, an evolutionary algorithm, obtaining a minimum MSE of 33 after 40 trials.</p

Scalability Analysis of the SOA-based All-optical Deep Neural Network

In this work we propose a noise model to investigate the scaling of the SOA-based all-optical deep neural networks regarding the number of WDM inputs and the cascading layers. The model is validated experimentally by emulating the OSNR evolution of the all-optical neuron. The results show that our all-optical neuron structure can be interconnected to establish a 16-input/neuron 16-neuron/layer 10-layer all-optical neural network with minor accuracy degradation for image classification

Scalability Analysis of the SOA-based All-optical Deep Neural Network

In this work we propose a noise model to investigate the scaling of the SOA-based all-optical deep neural networks regarding the number of WDM inputs and the cascading layers. The model is validated experimentally by emulating the OSNR evolution of the all-optical neuron. The results show that our all-optical neuron structure can be interconnected to establish a 16-input/neuron 16-neuron/layer 10-layer all-optical neural network with minor accuracy degradation for image classification

HFOS _L:hyper scale fast optical switch-based data center network with L-level sub-network

The ever-expanding growth of internet traffic enforces deployment of massive Data Center Networks (DCNs) supporting high performance communications. Optical switching is being studied as a promising approach to fulfill the surging requirements of large scale data centers. The tree-based optical topology limits the scalability of the interconnected network due to the limitations in the port count of optical switches and the lack of optical buffers. Alternatively, buffer-less Fast Optical Switch (FOS) was proposed to realize the nanosecond switching of optical DCNs. Although FOSs provide nanosecond optical switching, they still suffer from port count limitations to scale the DCN. To address the issue of scaling DCNs to more than two million servers, we propose the hyper scale FOS-based L-level DCNs (HFOSL) which is capable of building large networks with small radix switches. The numerical analysis shows L of 4 is the optimal level for HFOSL to obtain the lowest cost and power consumption. Specifically, under a network size of 160,000 servers, HFOS4 saves 36.2% in cost compared with the 2-level FOS-based DCN, while achieves 60% improvement for cost and 26.7% improvement for power consumption compared with Fat tree. Moreover, a wide range of simulations and analyses demonstrate that HFOS4 outperforms state-of-art FOS-based DCNs by up to 40% end-to-end latency under DCN size of 81920 servers.</p