We reconstruct a 48 Gbit/s nonlinearly distorted optical signal using an artificial neural network (ANN). The digital ANN execution exceeds traditional nonlinear equalizers, while its analog acceleration using plasmon...
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ISBN:
(纸本)9798350377583
We reconstruct a 48 Gbit/s nonlinearly distorted optical signal using an artificial neural network (ANN). The digital ANN execution exceeds traditional nonlinear equalizers, while its analog acceleration using plasmonic-organic-hybrid modulators surpasses conventional digital linear equalizers. (C) 2024 The Author(s)
This article proposes a convolutional neural network training algorithm based on frequency domain transformation to address the issues of high computational complexity and complex training processes in convolutional n...
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The human brain is the most complex circuit on the planet and the circuits inspired by the operation of the biological neuron are the most desired computing need. Artificial neuralnetworks (ANN) are circuits that can...
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The human brain is the most complex circuit on the planet and the circuits inspired by the operation of the biological neuron are the most desired computing need. Artificial neuralnetworks (ANN) are circuits that can replicate the biological neuron. opticalcomputing already doing wonders in integrated circuit technology and therefore the photonic implementation of neuralnetworks is one of the most appealing technologies of the current era due to its low power consumption and high bandwidth. The ANN models are designed as per the signal processing of the human brain therefore they can be used to improve the analytic power of any system. This article reviews the advancement in opticalneuralnetworks and their application for future perspective.
Three-dimensional opticalneuralnetworks (ONN) are a promising solution to the energy, time, and area yearning Artificial Intelligence (AI) hardware. The 3D additive manufacturing technique with Two-Photon Polymeriza...
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ISBN:
(纸本)9781510673533;9781510673526
Three-dimensional opticalneuralnetworks (ONN) are a promising solution to the energy, time, and area yearning Artificial Intelligence (AI) hardware. The 3D additive manufacturing technique with Two-Photon Polymerization (TPP) can be used to build the 3D dense ONN. In our work, we designed and fabricated the hybrid waveguide circuit which fuses the polymer and air clad waveguides, an important interconnect for the ONN. The polymer-cladded waveguide can support single mode and evanescent coupling while the air-cladded can support tight bend for dense integration.
Photonic neuralnetworks (PNNs) based on micro-ring resonators (MRRs) have attracted significant attention for their compactness and low power consumption. However, there remains substantial room for improvement in sp...
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Photonic neuralnetworks (PNNs) based on micro-ring resonators (MRRs) have attracted significant attention for their compactness and low power consumption. However, there remains substantial room for improvement in spectral density and network performance. Here, a novel PNN architecture is introduced based on double-stage serially coupled ring resonators (DCRRs), incorporating specially designed optoelectronic signal modulation and detection circuits, achieving a PNN with high spectral density, robustness, and accuracy. The DCRR achieves an extinction ratio of 55 dB and a narrow bandwidth of 0.17 nm. Through systematic innovation, it addresses the challenge of representing negative numbers in optoelectronic neuralnetworks caused by the non-negativity of light intensity, enabling positive and negative weighting operations using a single photodiode-based architecture. Experimental validation in digital and cell edge extraction and classification tasks demonstrates high accuracies above 95%. Compared to single-ring computing architectures with the same parameters, this method significantly reduces inter-channel crosstalk and spectral spacing, leading to a sixfold increase in spectral density and achieving a compute density of 2.48 TOPS/mm2. Furthermore, utilizing DCRR-based nonlinear activation results in faster convergence speed and higher recognition accuracy. The method provides the technical foundation for achieving high-density, high-precision photonic computing.
We propose a new on-chip opticalneural network (OONN) based on multimode interfer-ence-microring resonators (MMI-RRs). The suggested structure eliminates the need for wave-length division multiplexers (WDM) to create...
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We propose a new on-chip opticalneural network (OONN) based on multimode interfer-ence-microring resonators (MMI-RRs). The suggested structure eliminates the need for wave-length division multiplexers (WDM) to create an optical neuron on a single chip. New microring resonator structure based on 4x4 MMI coupler with a size of 24 & mu;m x 2900 & mu;m is used for the basic elements of the computation matrix, as a result a higher bandwidth and free spectral range (FSR) can be achieved. The Si3N4 platform along with the graphene sheet is designed to modu-late the signals and weights of the neuralnetworks at a very high speed. The Si3N4 can provide wide range of operating wavelengths and can work directly with the wavelengths of color imag-es. The structure's benefits include rapid computing speed, little loss, and the ability to handle both positive and negative values. The OONN has been applied to the MNIST dataset with a speed faster than 2.8 to 14x times compared with the conventional GPU methods.
Non-line-of-sight (NLOS) optical wireless communication (OWC) based on atmospheric scattering attracts more and more attention due to their mitigation of pointing, acquisition, and tracking requirement. However, the c...
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ISBN:
(纸本)9781665488679
Non-line-of-sight (NLOS) optical wireless communication (OWC) based on atmospheric scattering attracts more and more attention due to their mitigation of pointing, acquisition, and tracking requirement. However, the channel relies on scattering radiation and thus causes significant dispersion and inter-symbol interference (ISI), which limits high data rate communications. Reservoir computing (RC) is a computational framework derived from recurrent neuralnetworks (RNNs). It is suitable for sequential data processing with low training cost. In contrast to multilayer perceptron (MLP)-based symbol-wise detectors that treat ISI as noise, a RC performs sequence detection that takes ISI into account. In this paper, we propose to employ the RC approach to detect transmitted symbols of NLOS OWC systems without knowing channel state information. Our simulations show that the RC can perform symbol detection in various channel conditions and it brings lower bit error rate results than the MLP-based detection.
We show a new family of neuralnetworks based on the Schrodinger equation (SE-NET). In this analogy, the trainable weights of the neuralnetworks correspond to the physical quantities of the Schrodinger equation. Thes...
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We show a new family of neuralnetworks based on the Schrodinger equation (SE-NET). In this analogy, the trainable weights of the neuralnetworks correspond to the physical quantities of the Schrodinger equation. These physical quantities can be trained using the complex-valued adjoint method. Since the propagation of the SE-NET can be described by the evolution of physical systems, its outputs can be computed by using a physical solver. The trained network is transferable to actual optical systems. As a demonstration, we implemented the SE-NET with the Crank-Nicolson finite difference method on Pytorch. From the results of numerical simulations, we found that the performance of the SE-NET becomes better when the SE-NET becomes wider and deeper. However, the training of the SE-NET was unstable due to gradient explosions when SE-NET becomes deeper. Therefore, we also introduced phase-only training, which only updates the phase of the potential field (refractive index) in the Schrodinger equation. This enables stable training even for the deep SE-NET model because the unitarity of the system is kept under the training. In addition, the SE-NET enables a joint optimization of physical structures and digital neuralnetworks. As a demonstration, we performed a numerical demonstration of end-to-end machine learning (ML) with an optical frontend toward a compact spectrometer. Our results extend the application field of ML to hybrid physical-digital optimizations.
Artificial neuralnetworks (ANN) are a groundbreaking technology massively em-ployed in a plethora of fields. Currently, ANNs are mostly implemented through electronic digital computers, but analog photonic implementa...
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Artificial neuralnetworks (ANN) are a groundbreaking technology massively em-ployed in a plethora of fields. Currently, ANNs are mostly implemented through electronic digital computers, but analog photonic implementations are very interesting mainly because of low power consumption and high bandwidth. We recently demonstrated a photonic neuromorphic computing system based on frequency multiplexing that executes ANNs algorithms as reservoir computing and Extreme Learning Machines. Neuron signals are encoded in the amplitude of the lines of a frequency comb, and neuron interconnections are realized through frequency-domain interference. Here we present an integrated programmable spectral filter designed to manipulate the optical frequency comb in our frequency multiplexing neuromorphic computing platform. The programmable filter controls the attenuation of 16 independent wavelength channels with a 20 GHz spacing. We discuss the design and the results of the chip characterization, and we preliminary demonstrate, through a numerical simulation, that the produced chip is suitable for the envisioned neuromorphic computing application.& COPY;2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement
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