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 >18, the energy per operation is <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

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

Optical 4F Correlator for Acceleration of Convolutional Neural Networks

Convolutional neural networks (CNNs) represent one of the most effective methods for image classification. The de-facto approach for performing the required 2D convolutions is to run an iterative algorithm consisting of point wise multiplication and kernel shifting on a graphical processing unit (GPU) or tensor processing unit (TPU). However, the computational complexity of this algorithm is O(n2k2) for convolution of an (n × n) image and a (k × k) kernel, suggesting that 2D convolutions scale poorly for large matrices, leading to high power consumption and long execution times. A possible solution is a 4F optical correlator, which can, using Fourier optics, perform the convolutions in parallel and is not bound by conventional electronic limitations. In this paper we implement a 4F optical correlator using off-the-shelf components (Fig. 1) including spatial light modulators (SLMs) and a camera, while a PC is used to interact with the computing system. We experimentally demonstrate that a CNN utilizing such optical correlator has a best-case classification accuracy of 91% for the MNIST handwritten digit dataset and we show that the processing speed of the optical correlator can be in the same order of magnitude as a conventional GPU if maximum parallelism is exploited

From classical to quantum machine learning: survey on routing optimization in 6G software defined networking

The sixth generation (6G) of mobile networks will adopt on-demand self-reconfiguration to fulfill simultaneously stringent key performance indicators and overall optimization of usage of network resources. Such dynamic and flexible network management is made possible by Software Defined Networking (SDN) with a global view of the network, centralized control, and adaptable forwarding rules. Because of the complexity of 6G networks, Artificial Intelligence and its integration with SDN and Quantum Computing are considered prospective solutions to hard problems such as optimized routing in highly dynamic and complex networks. The main contribution of this survey is to present an in-depth study and analysis of recent research on the application of Reinforcement Learning (RL), Deep Reinforcement Learning (DRL), and Quantum Machine Learning (QML) techniques to address SDN routing challenges in 6G networks. Furthermore, the paper identifies and discusses open research questions in this domain. In summary, we conclude that there is a significant shift toward employing RL/DRL-based routing strategies in SDN networks, particularly over the past 3 years. Moreover, there is a huge interest in integrating QML techniques to tackle the complexity of routing in 6G networks. However, considerable work remains to be done in both approaches in order to accomplish thorough comparisons and synergies among various approaches and conduct meaningful evaluations using open datasets and different topologies

Organic Light‐Emitting Nanofibers by Solvent‐Resistant Nanofluidics

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Soft Nanopatterning on Light‐Emitting Inorganic‐Organic Composites

In this work we demonstrate the nanopatterning of nanocomposites made by luminescent zinc oxide nanoparticles and light-emitting conjugated polymers by means of soft molding lithography. Vertical nanofluidics is exploited to overcome the polymer transport difficulties intrinsic in materials incorporating nanocrystals, and the rheology, fluorescence, absolute quantum yield, and emission directionality of the nanostructured composites are investigated. We study the effect of patterned gratings on the directionality of light emitted from the nanocomposites, finding evidence of the enhancement of forward emitted light, due to the printed wavelength-scale periodicity. These results open new possibilities for the realization of nanopatterned devices based on hybrid organic-inorganic systems

Imprinting strategies for 100 nm lithography on polyfluorene and poly(phenylenevinylene) derivatives and their blends

Abstract We report on the use of nanoimprint lithography at room temperature (RT-NIL) for the direct structuring of polyfluorene and poly(phenylenevinylene) derivatives and of their blends without the degradation of the emissive characteristics of the active molecules. We apply RT-NIL for the fabrication of periodic one- and two-dimensional gratings with feature width that varies from 100 to 500 nm. Moreover, we analysed the effects that a superimposed periodic corrugation induces on the emitted light in terms of spectral properties and luminescence efficiency, thus ruling out any degradation of the emission. In particular, the combination of nanopatterning and active blends opens new perspectives for the control of the emitted colour from conjugated polymer films

From classical to quantum machine learning: survey on routing optimization in 6G software defined networking

The sixth generation (6G) of mobile networks will adopt on-demand self-reconfiguration to fulfill simultaneously stringent key performance indicators and overall optimization of usage of network resources. Such dynamic and flexible network management is made possible by Software Defined Networking (SDN) with a global view of the network, centralized control, and adaptable forwarding rules. Because of the complexity of 6G networks, Artificial Intelligence and its integration with SDN and Quantum Computing are considered prospective solutions to hard problems such as optimized routing in highly dynamic and complex networks. The main contribution of this survey is to present an in-depth study and analysis of recent research on the application of Reinforcement Learning (RL), Deep Reinforcement Learning (DRL), and Quantum Machine Learning (QML) techniques to address SDN routing challenges in 6G networks. Furthermore, the paper identifies and discusses open research questions in this domain. In summary, we conclude that there is a significant shift toward employing RL/DRL-based routing strategies in SDN networks, particularly over the past 3 years. Moreover, there is a huge interest in integrating QML techniques to tackle the complexity of routing in 6G networks. However, considerable work remains to be done in both approaches in order to accomplish thorough comparisons and synergies among various approaches and conduct meaningful evaluations using open datasets and different topologies