Готові списки джерел за темами / Photonic Neural Network

Добірка наукової літератури з теми "Photonic Neural Network"

Автор: Grafiati
Опубліковано: 6 вересня 2023
Оновлено: 7 вересня 2023

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Статті в журналах з теми "Photonic Neural Network"

1

Han, Yanan, Shuiying Xiang, Yuna Zhang, Shuang Gao, Aijun Wen, and Yue Hao. "An All-MRR-Based Photonic Spiking Neural Network for Spike Sequence Learning." Photonics 9, no. 2 (February 20, 2022): 120. http://dx.doi.org/10.3390/photonics9020120.

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Анотація:
Photonic spiking neural networks (SNN) have the advantages of high power efficiency, high bandwidth and low delay, but limitations are encountered in large-scale integration. The silicon photonics platform is a promising candidate for realizing large-scale photonic SNN because it is compatible with the current mature CMOS platforms. Here, we present an architecture of photonic SNN which consists of photonic neuron, photonic spike timing dependent plasticity (STDP) and weight configuration that are all based on silicon micro-ring resonators (MRRs), via taking advantage of the nonlinear effects in silicon. The photonic spiking neuron based on the add-drop MRR is proposed, and a system-level computational model of all-MRR-based photonic SNN is presented. The proposed architecture could exploit the properties of small area, high integration and flexible structure of MRR, but also faces challenges caused by the high sensitivity of MRR. The spike sequence learning problem is addressed based on the proposed all-MRR-based photonic SNN architecture via adopting supervised training algorithms. We show the importance of algorithms when hardware devices are limited.
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2

Marquez, Bicky A., Hugh Morison, Zhimu Guo, Matthew Filipovich, Paul R. Prucnal, and Bhavin J. Shastri. "Graphene-based photonic synapse for multi wavelength neural networks." MRS Advances 5, no. 37-38 (2020): 1909–17. http://dx.doi.org/10.1557/adv.2020.327.

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Анотація:
AbstractA synapse is a junction between two biological neurons, and the strength, or weight of the synapse, determines the communication strength between the neurons. Building a neuromorphic (i.e. neuron isomorphic) computing architecture, inspired by a biological network or brain, requires many engineered synapses. Furthermore, recent investigation in neuromorphic photonics, i.e. neuromorphic architectures on photonics platforms, have garnered much interest to enable high-bandwidth, low-latency, low-energy applications of neural networks in machine learning and neuromorphic computing. We propose a graphene-based synapse model as a core element to enable large-scale photonic neural networks based on on-chip multiwavelength techniques. This device consists of an electro-absorption modulator embedded in a microring resonator. We also introduce an encoding protocol that allows for the representation of synaptic weights on our photonic device with 15.7 bits of resolution using current control hardware. Recent work has suggested that graphene-based modulators could operate in excess of 100 GHz. Combined with our work, such a graphene-based synapse could enable applications for ultrafast and online learning.
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Wang, Ruiting, Pengfei Wang, Chen Lyu, Guangzhen Luo, Hongyan Yu, Xuliang Zhou, Yejin Zhang, and Jiaoqing Pan. "Multicore Photonic Complex-Valued Neural Network with Transformation Layer." Photonics 9, no. 6 (May 28, 2022): 384. http://dx.doi.org/10.3390/photonics9060384.

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Анотація:
Photonic neural network chips have been widely studied because of their low power consumption, high speed and large bandwidth. Using amplitude and phase to encode, photonic chips can accelerate complex-valued neural network computations. In this article, a photonic complex-valued neural network (PCNN) chip is designed. The scale of the single-core PCNN chip is limited because of optical losses, and the multicore architecture of the chip is used to improve computing capability. Further, for improving the performance of the PCNN, we propose the transformation layer, which can be implemented by the designed photonic chip to transform real-valued encoding to complex-valued encoding, which has richer information. Compared with real-valued input, the transformation layer can effectively improve the classification accuracy from 93.14% to 97.51% of a 64-dimensional input on the MNIST test set. Finally, we analyze the multicore computation of the PCNN. Compared with the single-core architecture, the multicore architecture can improve the classification accuracy by implementing larger neural networks and has better phase noise robustness. The proposed architecture and algorithms are beneficial to promote the accelerated computing of photonic chips for complex-valued neural networks and are promising for use in many applications, such as image recognition and signal processing.
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4

Ferreira de Lima, Thomas, Bhavin J. Shastri, Alexander N. Tait, Mitchell A. Nahmias, and Paul R. Prucnal. "Progress in neuromorphic photonics." Nanophotonics 6, no. 3 (March 11, 2017): 577–99. http://dx.doi.org/10.1515/nanoph-2016-0139.

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Анотація:
AbstractAs society’s appetite for information continues to grow, so does our need to process this information with increasing speed and versatility. Many believe that the one-size-fits-all solution of digital electronics is becoming a limiting factor in certain areas such as data links, cognitive radio, and ultrafast control. Analog photonic devices have found relatively simple signal processing niches where electronics can no longer provide sufficient speed and reconfigurability. 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. By bridging the mathematical prowess of artificial neural networks to the underlying physics of optoelectronic devices, neuromorphic photonics could breach new domains of information processing demanding significant complexity, low cost, and unmatched speed. In this article, we review the progress in neuromorphic photonics, focusing on photonic integrated devices. The challenges and design rules for optoelectronic instantiation of artificial neurons are presented. The proposed photonic architecture revolves around the processing network node composed of two parts: a nonlinear element and a network interface. We then survey excitable lasers in the recent literature as candidates for the nonlinear node and microring-resonator weight banks as the network interface. Finally, we compare metrics between neuromorphic electronics and neuromorphic photonics and discuss potential applications.
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Pai, Sunil, Zhanghao Sun, Tyler W. Hughes, Taewon Park, Ben Bartlett, Ian A. D. Williamson, Momchil Minkov, et al. "Experimentally realized in situ backpropagation for deep learning in photonic neural networks." Science 380, no. 6643 (April 28, 2023): 398–404. http://dx.doi.org/10.1126/science.ade8450.

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Анотація:
Integrated photonic neural networks provide a promising platform for energy-efficient, high-throughput machine learning with extensive scientific and commercial applications. Photonic neural networks efficiently transform optically encoded inputs using Mach-Zehnder interferometer mesh networks interleaved with nonlinearities. We experimentally trained a three-layer, four-port silicon photonic neural network with programmable phase shifters and optical power monitoring to solve classification tasks using “in situ backpropagation,” a photonic analog of the most popular method to train conventional neural networks. We measured backpropagated gradients for phase-shifter voltages by interfering forward- and backward-propagating light and simulated in situ backpropagation for 64-port photonic neural networks trained on MNIST image recognition given errors. All experiments performed comparably to digital simulations ( > 94% test accuracy), and energy scaling analysis indicated a route to scalable machine learning.
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6

Fu, Chentao, Shuiying Xiang, Yanan Han, Ziwei Song, and Yue Hao. "Multilayer Photonic Spiking Neural Networks: Generalized Supervised Learning Algorithm and Network Optimization." Photonics 9, no. 4 (March 25, 2022): 217. http://dx.doi.org/10.3390/photonics9040217.

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Анотація:
We propose a generalized supervised learning algorithm for multilayer photonic spiking neural networks (SNNs) by combining the spike-timing dependent plasticity (STDP) rule and the gradient descent mechanism. A vertical-cavity surface-emitting laser with an embedded saturable absorber (VCSEL-SA) is employed as a photonic leaky-integrate-and-fire (LIF) neuron. The temporal coding strategy is employed to transform information into the precise firing time. With the modified supervised learning algorithm, the trained multilayer photonic SNN successfully solves the XOR problem and performs well on the Iris and Wisconsin breast cancer datasets. This indicates that a generalized supervised learning algorithm is realized for multilayer photonic SNN. In addition, network optimization is performed by considering different network sizes.
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Xia, Chengpeng, Yawen Chen, Haibo Zhang, Hao Zhang, Fei Dai, and Jigang Wu. "Efficient neural network accelerators with optical computing and communication." Computer Science and Information Systems, no. 00 (2022): 66. http://dx.doi.org/10.2298/csis220131066x.

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Анотація:
Conventional electronic Artificial Neural Networks (ANNs) accelerators focus on architecture design and numerical computation optimization to improve the training efficiency. However, these approaches have recently encountered bottlenecks in terms of energy efficiency and computing performance, which leads to an increase interest in photonic accelerator. Photonic architectures with low energy consumption, high transmission speed and high bandwidth have been considered as an important role for generation of computing architectures. In this paper, to provide a better understanding of optical technology used in ANN acceleration, we present a comprehensive review for the efficient photonic computing and communication in ANN accelerators. The related photonic devices are investigated in terms of the application in ANNs acceleration, and a classification of existing solutions is proposed that are categorized into optical computing acceleration and optical communication acceleration according to photonic effects and photonic architectures. Moreover, we discuss the challenges for these photonic neural network acceleration approaches to highlight the most promising future research opportunities in this field.
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Christensen, Thomas, Charlotte Loh, Stjepan Picek, Domagoj Jakobović, Li Jing, Sophie Fisher, Vladimir Ceperic, John D. Joannopoulos, and Marin Soljačić. "Predictive and generative machine learning models for photonic crystals." Nanophotonics 9, no. 13 (June 29, 2020): 4183–92. http://dx.doi.org/10.1515/nanoph-2020-0197.

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Анотація:
AbstractThe prediction and design of photonic features have traditionally been guided by theory-driven computational methods, spanning a wide range of direct solvers and optimization techniques. Motivated by enormous advances in the field of machine learning, there has recently been a growing interest in developing complementary data-driven methods for photonics. Here, we demonstrate several predictive and generative data-driven approaches for the characterization and inverse design of photonic crystals. Concretely, we built a data set of 20,000 two-dimensional photonic crystal unit cells and their associated band structures, enabling the training of supervised learning models. Using these data set, we demonstrate a high-accuracy convolutional neural network for band structure prediction, with orders-of-magnitude speedup compared to conventional theory-driven solvers. Separately, we demonstrate an approach to high-throughput inverse design of photonic crystals via generative adversarial networks, with the design goal of substantial transverse-magnetic band gaps. Our work highlights photonic crystals as a natural application domain and test bed for the development of data-driven tools in photonics and the natural sciences.
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Zhang, Lulu, Yongzhi Zhang, Furong Liu, Qingyuan Chen, Yangbo Lian, and Quanlong Ma. "On-Chip Photonic Synapses with All-Optical Memory and Neural Network Computation." Micromachines 14, no. 1 (December 27, 2022): 74. http://dx.doi.org/10.3390/mi14010074.

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Анотація:
Inspired by the human brain, neural network computing was expected to break the bottleneck of traditional computing, but the integrated design still faces great challenges. Here, a readily integrated membrane-system photonic synapse was demonstrated. By pre-pulse training at 1064 nm (cutoff wavelength), the photonic synapse can be regulated both excitatory and inhibitory at tunable wavelengths (1200–2000 nm). Furthermore, more weights and memory functions were shown through the photonic synapse integrated network. Additionally, the digital recognition function of the single-layer perceptron neural network constructed by photonic synapses has been successfully demonstrated. Most of the biological synaptic functions were realized by the photonic synaptic network, and it had the advantages of compact structure, scalable, adjustable wavelength, and so on, which opens up a new idea for the study of the neural synaptic network.
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Quan, Zhiqiang, Yuanjian Wan, and Jian Wang. "On-chip ultra-compact nonvolatile photonic synapse." Applied Physics Letters 121, no. 17 (October 24, 2022): 171102. http://dx.doi.org/10.1063/5.0115564.

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Анотація:
The important research content of modern communication systems is to realize high-speed, stable, and intelligent information transmission and processing. All-optical neural networks based on the silicon integrated technology and phase change materials (PCMs) can realize picosecond-level modulation speed, faster processing speed, and lower energy consumption compared with the traditional electrical communication system. The photonic synapse is the core component of the all-optical neural network module, but the existing photonic synapses based on PCMs require a modulation distance (MD) of several micrometers or even ten micrometers to achieve a large output intensity range. In this paper, we propose an ultra-compact nonvolatile photonic synapse, in which MD can be shortened to 1 μm, breaking the record of the minimum signal MD of the silicon photonic synapse using the PCMs. At the same time, the output intensity range of our synapse is almost twice that of the existing research. Based on this photonic synapse, we analyze the relationship between the output response and incident wavelength, which can help to design an ultra-compact photonic convolutional neural network. This work has great potential in future photonic computing and photonic communication technologies.
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Більше джерел

Дисертації з теми "Photonic Neural Network"

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Yang, Gang. "Compact Photonic Integrated Passive Circuits." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/26958.

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Анотація:
Photonic Integrated Circuits (PICs) based on silicon photonics have received great interest due to the low loss caused by the high-refractive-index contrast and the complementary metal-oxide semiconductor compatibility. The need for high-density, high-yield, low-cost, low-power consumption, and large-scale on-chip photonic integration requires the technologies to further minimize the size while exhibiting high performance. Moreover, the fast development and expansion of silicon photonics devices for different applications and functionalities require effective design approaches to optimize the device performance while reducing the design complexity. In this thesis, several fundamental components for PICs are presented as the building blocks for advanced photonic circuits. To test the effectiveness of the design, Mach–Zehnder interferometers are simulated and fabricated on a Silicon-on-Insulator (SOI) platform, which shows a good agreement between the experimental and simulation results. Moreover, compact vertical grating couplers with broad optical bandwidth are studied. Experimental results show the compact size and the light coupling capabilities. Multimode Interference (MMI) splitter acts as one critical component in PICs. However, the minimum requirement of mid-to-mid channel spacing to avoid crosstalk limits the MMI size to be further reduced and thus limits the component density in the photonic integration. To solve this problem, a compact SOI MMI power splitter based on optical strip barriers is presented to achieve high crosstalk reduction. Three different MMI power splitters are designed and simulated with an ultra-small device footprint, high uniformity, while maintaining a low insertion loss of 0.4dB. Inverse design methods with different optimization algorithms are utilized to design compact and high-performance PIC components. Firstly, a sequential least-squares programming algorithm is introduced to inverse design a waveguide crossing. This gradient-based algorithm is suitable for simple structures with fewer parameters, or a good starting point can be obtained from experience or physical theories. Secondly, a novel dynamic iterative batch optimization method is presented in the thesis to design a high-performance segmented mode expander. In the simulation, the optimized structure achieves a coupling efficiency of 81% for TE polarization at the wavelength of 1550nm. It also shows a simulated transmission loss of lower than -1.137dB within 60nm bandwidth. This approach paves the way for the rapid design of PIC components with a compact footprint. Additionally, a Direct Binary Search (DBS) algorithm is introduced for designing pixel-like structures with binary-value-represented topology patterns, where a 3dB beam splitter is used in the design. DBS algorithm can be utilized to generate a high-quality dataset used for deep learning acceleration methods. To solve the time-efficiency and non-scalable issues of conventional inverse design methods, a neural network-based inverse design approach is presented and applied on the design of a wavelength demultiplexer structure. The method solves the data domain shift problem that existed in the conventional tandem network architecture and improves the prediction accuracy with a 99% validation accuracy. It also shows high stability and robustness to the quantity and quality of training data. The demonstrated wavelength demultiplexer has an ultra-compact footprint of 2.6×2.6μm2, a high transmission efficiency with a transmission loss of -2dB, and a low crosstalk around -7dB simultaneously.
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2

Baylon, Fuentes Antonio. "Ring topology of an optical phase delayed nonlinear dynamics for neuromorphic photonic computing." Thesis, Besançon, 2016. http://www.theses.fr/2016BESA2047/document.

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Анотація:
Aujourd'hui, la plupart des ordinateurs sont encore basés sur des concepts développés il y a plus de 60 ans par Alan Turing et John von Neumann. Cependant, ces ordinateurs numériques ont déjà commencé à atteindre certaines limites physiques via la technologie de la microélectronique au silicium (dissipation, vitesse, limites d'intégration, consommation d'énergie). Des approches alternatives, plus puissantes, plus efficaces et moins consommatrices d'énergie, constituent depuis plusieurs années un enjeu scientifique majeur. Beaucoup de ces approches s'inspirent naturellement du cerveau humain, dont les principes opérationnels sont encore loin d'être compris. Au début des années 2000, la communauté scientifique s'est aperçue qu'une modification du réseau neuronal récurrent (RNN), plus simple et maintenant appelée Reservoir Computing (RC), est parfois plus efficace pour certaines fonctionnalités, et est un nouveau paradigme de calcul qui s'inspire du cerveau. Sa structure est assez semblable aux concepts classiques de RNN, présentant généralement trois parties: une couche d'entrée pour injecter l'information dans un système dynamique non-linéaire (Write-In), une seconde couche où l'information d'entrée est projetée dans un espace de grande dimension (appelé réservoir dynamique) et une couche de sortie à partir de laquelle les informations traitées sont extraites par une fonction dite de lecture-sortie. Dans l'approche RC, la procédure d'apprentissage est effectuée uniquement dans la couche de sortie, tandis que la couche d'entrée et la couche réservoir sont fixées de manière aléatoire, ce qui constitue l'originalité principale du RC par rapport aux méthodes RNN. Cette fonctionnalité permet d'obtenir plus d'efficacité, de rapidité, de convergence d'apprentissage, et permet une mise en œuvre expérimentale. Cette thèse de doctorat a pour objectifs d'implémenter pour la première fois le RC photoniques en utilisant des dispositifs de télécommunication. Notre mise en œuvre expérimentale est basée sur un système dynamique non linéaire à retard, qui repose sur un oscillateur électro-optique (EO) avec une modulation de phase différentielle. Cet oscillateur EO a été largement étudié dans le contexte de la cryptographie optique du chaos. La dynamique présentée par de tels systèmes est en effet exploitée pour développer des comportements complexes dans un espace de phase à dimension infinie, et des analogies avec la dynamique spatio-temporelle (tels que les réseaux neuronaux) sont également trouvés dans la littérature. De telles particularités des systèmes à retard ont conforté l'idée de remplacer le RNN traditionnel (généralement difficile à concevoir technologiquement) par une architecture à retard d'EO non linéaire. Afin d'évaluer la puissance de calcul de notre approche RC, nous avons mis en œuvre deux tests de reconnaissance de chiffres parlés (tests de classification) à partir d'une base de données standard en intelligence artificielle (TI-46 et AURORA-2), et nous avons obtenu des performances très proches de l'état de l'art tout en établissant un nouvel état de l'art en ce qui concerne la vitesse de classification. Notre approche RC photonique nous a en effet permis de traiter environ 1 million de mots par seconde, améliorant la vitesse de traitement de l'information d'un facteur supérieur à ~3
Nowadays most of computers are still based on concepts developed more than 60 years ago by Alan Turing and John von Neumann. However, these digital computers have already begun to reach certain physical limits of their implementation via silicon microelectronics technology (dissipation, speed, integration limits, energy consumption). Alternative approaches, more powerful, more efficient and with less consume of energy, have constituted a major scientific issue for several years. Many of these approaches naturally attempt to get inspiration for the human brain, whose operating principles are still far from being understood. In this line of research, a surprising variation of recurrent neural network (RNN), simpler, and also even sometimes more efficient for features or processing cases, has appeared in the early 2000s, now known as Reservoir Computing (RC), which is currently emerging new brain-inspired computational paradigm. Its structure is quite similar to the classical RNN computing concepts, exhibiting generally three parts: an input layer to inject the information into a nonlinear dynamical system (Write-In), a second layer where the input information is projected in a space of high dimension called dynamical reservoir and an output layer from which the processed information is extracted through a so-called Read-Out function. In RC approach the learning procedure is performed in the output layer only, while the input and reservoir layer are randomly fixed, being the main originality of RC compared to the RNN methods. This feature allows to get more efficiency, rapidity and a learning convergence, as well as to provide an experimental implementation solution. This PhD thesis is dedicated to one of the first photonic RC implementation using telecommunication devices. Our experimental implementation is based on a nonlinear delayed dynamical system, which relies on an electro-optic (EO) oscillator with a differential phase modulation. This EO oscillator was extensively studied in the context of the optical chaos cryptography. Dynamics exhibited by such systems are indeed known to develop complex behaviors in an infinite dimensional phase space, and analogies with space-time dynamics (as neural network ones are a kind of) are also found in the literature. Such peculiarities of delay systems supported the idea of replacing the traditional RNN (usually difficult to design technologically) by a nonlinear EO delay architecture. In order to evaluate the computational power of our RC approach, we implement two spoken digit recognition tests (classification tests) taken from a standard databases in artificial intelligence TI-46 and AURORA-2, obtaining results very close to state-of-the-art performances and establishing state-of-the-art in classification speed. Our photonic RC approach allowed us to process around of 1 million of words per second, improving the information processing speed by a factor ~3
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3

Vinckier, Quentin. "Analog bio-inspired photonic processors based on the reservoir computing paradigm." Doctoral thesis, Universite Libre de Bruxelles, 2016. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/237069.

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Анотація:
For many challenging problems where the mathematical description is not explicitly defined, artificial intelligence methods appear to be much more robust compared to traditional algorithms. Such methods share the common property of learning from examples in order to “explore” the problem to solve. Then, they generalize these examples to new and unseen input signals. The reservoir computing paradigm is a bio-inspired approach drawn from the theory of artificial Recurrent Neural Networks (RNNs) to process time-dependent data. This machine learning method was proposed independently by several research groups in the early 2000s. It has enabled a breakthrough in analog information processing, with several experiments demonstrating state-of-the-art performance for a wide range of hard nonlinear tasks. These tasks include for instance dynamic pattern classification, grammar modeling, speechrecognition, nonlinear channel equalization, detection of epileptic seizures, robot control, timeseries prediction, brain-machine interfacing, power system monitoring, financial forecasting, or handwriting recognition. A Reservoir Computer (RC) is composed of three different layers. There is first the neural network itself, called “reservoir”, which consists of a large number of internal variables (i.e. reservoir states) all interconnected together to exchange information. The internal dynamics of such a system, driven by a function of the inputs and the former reservoir states, is thus extremely rich. Through an input layer, a time-dependent input signal is applied to all the internal variables to disturb the neural network dynamics. Then, in the output layer, all these reservoir states are processed, often by taking a linear combination thereof at each time-step, to compute the output signal. Let us note that the presence of a non-linearity somewhere in the system is essential to reach high performance computing on nonlinear tasks. The principal novelty of the reservoir computing paradigm was to propose an RNN where most of the connection weights are generated randomly, except for the weights adjusted to compute the output signal from a linear combination of the reservoir states. In addition, some global parameters can be tuned to get the best performance, depending on the reservoir architecture and on the task. This simple and easy process considerably decreases the training complexity compared to traditional RNNs, for which all the weights needed to be optimized. RC algorithms can be programmed using modern traditional processors. But these electronic processors are better suited to digital processing for which a lot of transistors continuously need to be switched on and off, leading to higher power consumption. As we can intuitively understand, processors with hardware directly dedicated to RC operations – in otherwords analog bio-inspired processors – could be much more efficient regarding both speed and power consumption. Based on the same idea of high speed and low power consumption, the last few decades have seen an increasing use of coherent optics in the transport of information thanks to its high bandwidth and high power efficiency advantages. In order to address the future challenge of high performance, high speed, and power efficient nontrivial computing, it is thus natural to turn towards optical implementations of RCs using coherent light. Over the last few years, several physical implementations of RCs using optics and (opto)electronics have been successfully demonstrated. In the present PhD thesis, the reservoirs are based on a large coherently driven linear passive fiber cavity. The internal states are encoded by time-multiplexing in the cavity. Each reservoir state is therefore processed sequentially. This reservoir architecture exhibits many qualities that were either absent or not simultaneously present in previous works: we can perform analog optical signal processing; the easy tunability of each key parameter achieves the best operating point for each task; the system is able to reach a strikingly weak noise floor thanks to the absence of active elements in the reservoir itself; a richer dynamics is provided by operating in coherent light, as the reservoir states are encoded in both the amplitude and the phase of the electromagnetic field; high power efficiency is obtained as a result of the passive nature and simplicity of the setup. However, it is important to note that at this stage we have only obtained low optical power consumption for the reservoir itself. We have not tried to minimize the overall power consumption, including all control electronics. The first experiment reported in chapter 4 uses a quadratic non-linearity on each reservoir state in the output layer. This non-linearity is provided by a readout photodiode since it produces a current proportional to the intensity of the light. On a number of benchmark tasks widely used in the reservoir computing community, the error rates demonstrated with this RC architecture – both in simulation and experimentally – are, to our knowledge, the lowest obtained so far. Furthermore, the analytic model describing our experiment is also of interest, asit constitutes a very simple high performance RC algorithm. The setup reported in chapter 4 requires offline digital post-processing to compute its output signal by summing the weighted reservoir states at each time-step. In chapter 5, we numerically study a realistic model of an optoelectronic “analog readout layer” adapted on the setup presented in chapter 4. This readout layer is based on an RLC low-pass filter acting as an integrator over the weighted reservoir states to autonomously generate the RC output signal. On three benchmark tasks, we obtained very good simulation results that need to be confirmed experimentally in the future. These promising simulation results pave the way for standalone high performance physical reservoir computers.The RC architecture presented in chapter 5 is an autonomous optoelectronic implementation able to electrically generate its output signal. In order to contribute to the challenge of all-optical computing, chapter 6 highlights the possibility of processing information autonomously and optically using an RC based on two coherently driven passive linear cavities. The first one constitutes the reservoir itself and pumps the second one, which acts as an optical integrator onthe weighted reservoir states to optically generate the RC output signal after sampling. A sine non-linearity is implemented on the input signal, whereas both the reservoir and the readout layer are kept linear. Let us note that, because the non-linearity in this system is provided by a Mach-Zehnder modulator on the input signal, the input signal of this RC configuration needs to be an electrical signal. On the contrary, the RC implementation presented in chapter 5 processes optical input signals, but its output is electrical. We obtained very good simulation results on a single task and promising experimental results on two tasks. At the end of this chapter, interesting perspectives are pointed out to improve the performance of this challenging experiment. This system constitutes the first autonomous photonic RC able to optically generate its output signal.
Doctorat en Sciences de l'ingénieur et technologie
info:eu-repo/semantics/nonPublished
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4

Maktoobi, Sheler. "Couplage diffractif pour réseaux de neurones optiques." Thesis, Bourgogne Franche-Comté, 2020. http://www.theses.fr/2020UBFCD019.

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Анотація:
Les réseaux photoniques à haute performance peuvent être considérés comme des supports pour les futurs systèmes de calcul. Contrairement à l'électronique, les systèmes photoniques offrent des avantages intéressants, par exemple la possibilité de réaliser des réseaux complètement parallèles. Récemment, les réseaux de neurones ont attiré l'attention de la communauté photonique. L'une des difficultés les plus importantes, en matière de réseaux photoniques parallèles à grande échelle, est la réalisation des connexions. La diffraction est exploitée ici comme méthode pour traiter les connexions entre les nœuds (couplage) dans les réseaux de neurones optiques. Dans cette thèse, nous étudions l'extensibilité d'un couplage diffractif en détails de la façon suivante :Tout d'abord, nous commençons par une introduction générale à propos de l'intelligence artificielle, de l'apprentissage machine, des réseaux de neurones artificiels et des réseaux de neurones photoniques. Lors de la conception d'un réseau neuronal fonctionnel, les règles de l'apprentissage machine sont des éléments essentiels pour optimiser une configuration et ainsi obtenir une faible erreur du système, donc les règles de l'apprentissage sont introduites (chapitre 1). Nous étudions les concepts fondamentaux du couplage diffractif dans notre réservoir spatio-temporel. Dans ce cas, la théorie de la diffraction est expliquée. Nous utilisons un schéma analytique pour fournir les limites en termes de taille des réseaux diffractifs, qui font partie intégrante de notre réseau neuronal photonique (chapitre 2). Les concepts du couplage diffractif sont étudiés expérimentalement dans deux expériences différentes afin de confirmer les limites obtenues analytiquement, et pour déterminer le nombre maximum de nœuds pouvant être couplés dans le réseau photonique (Chapitre 3). Les simulations numériques d'une telle expérience sont basées sur deux schémas différents pour calculer numériquement la taille maximale du réseau, qui approche une surface de 100 mm2 (chapitre 4). Enfin, l'ensemble du réseau neuronal photonique est démontré. Nous concevons un réservoir spatialement étendu sur 900 nœuds. En conséquence, notre système généralise la prédiction pour la séquence chaotique de Mackey-Glass (chapitre 5)
Photonic networks with high performance can be considered as substrates for future computing systems. In comparison with electronics, photonic systems have substantial privileges, for instance the possibility of a fully parallel implementation of networks. Recently, neural networks have moved into the center of attention of the photonic community. One of the most important requirements for parallel large-scale photonic networks is to realize the connectivities. Diffraction is considered as a method to process the connections between the nodes (coupling) in optical neural networks. In the current thesis, we evaluate the scalability of a diffractive coupling in more details as follow:First, we begin with a general introductions for artificial intelligence, machine learning, artificial neural network and photonic neural networks. To establish a working neural network, learning rules are an essential part to optimize a configuration for obtaining a low error from the system, hence learning rules are introduced (Chapter 1). We investigate the fundamental concepts of diffractive coupling in our spatio-temporal reservoir. In that case, theory of diffraction is explained. We use an analytical scheme to provide the limits for the size of diffractive networks which is a part of our photonic neural network (Chapter 2). The concepts of diffractive coupling are investigated experimentally by two different experiments to confirm the analytical limits and to obtain maximum number of nodes which can be coupled in the photonic network (Chapter 3). Numerical simulations for such an experimental setup is modeled in two different schemes to obtain the maximum size of network numerically, which approaches a surface of 100 mm2 (Chapter 4). Finally, the complete photonic neural network is demonstrated. We design a spatially extended reservoir for 900 nodes. Consequently, our system generalizes the prediction for the chaotic Mackey–Glass sequence (Chapter 5)
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5

Bazzanella, Davide. "Microring Based Neuromorphic Photonics." Doctoral thesis, Università degli studi di Trento, 2022. http://hdl.handle.net/11572/344624.

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This manuscript investigates the use of microring resonators to create all-optical reservoir-computing networks implemented in silicon photonics. Artificial neural networks and reservoir-computing are promising applications for integrated photonics, as they could make use of the bandwidth and the intrinsic parallelism of optical signals. This work mainly illustrates two aspects: the modelling of photonic integrated circuits and the experimental results obtained with all-optical devices. The modelling of photonic integrated circuits is examined in detail, both concerning fundamental theory and from the point of view of numerical simulations. In particular, the simulations focus on the nonlinear effects present in integrated optical cavities, which increase the inherent complexity of their optical response. Toward this objective, I developed a new numerical tool, precise, which can simulate arbitrary circuits, taking into account both linear propagation and nonlinear effects. The experimental results concentrate on the use of SCISSORs and a single microring resonator as reservoirs and the complex perceptron scheme. The devices have been extensively tested with logical operations, achieving bit error rates of less than 10^−5 at 16 Gbps in the case of the complex perceptron. Additionally, an in-depth explanation of the experimental setup and the description of the manufactured designs are provided. The achievements reported in this work mark an encouraging first step in the direction of the development of novel networks that employ the full potential of all-optical devices.
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6

Skirlo, Scott Alexander. "Photonics for technology : circuits, chip-scale LIDAR, and optical neural networks." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112519.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Physics, 2017.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 163-175).
This thesis focuses on a wide range of contemporary topics in modern electromagnetics and technology including topologically protected one-way modes, integrated photonic LIDAR, and optical neural networks. First, we numerically investigate large Chern numbers in photonic crystals and explore their origin from simultaneously gapping multiple band degeneracies. Following this, we perform microwave transmission measurements in the bulk and at the edge of ferrimagnetic photonic crystals. Bandgaps with large Chern numbers of 2, 3, and 4 are present in the experimental results 'which show excellent agreement with theory. We measure the mode profiles and Fourier transform them to produce dispersion relations of the edge modes, whose number and direction match our Chern number calculations. We use these waveguides to realize reflectionless power splitters and outline their application to general one-way circuits. Next we create a new chip-scale LIDAR architecture in analogy to planar RF lenses. Instead of relying upon many continuously tuned thermal phase shifters to implement nonmechanical beam steering, we use aplanatic lenses excited in their focal plane feeding ID gratings to generate discrete beams. We design devices which support up to 128 resolvable points in-plane and 80 resolvable points out-of-plane, which are currently being fabricated and tested. These devices have many advantages over conventional optical phased arrays including greatly increased optical output power and decreased electrical power for in-plane beamforming. Finally we explore a new approach for implementing convolutional neural networks through an integrated photonics circuit consisting of Mach-Zehnder Interferometers, optical delay lines, and optical nonlinearity units. This new platform, should be able to perform the order of a thousand inferences per second, at [mu]J power levels per inference, with the nearest state of the art ASIC and GPU competitors operating 30 times slower and requiring three orders of magnitude more power.
by Scott Alexander Skirlo.
Ph. D.
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7

Chamanirad, Mohsen. "Design and implementation of controller for robotic manipulators using Artificial Neural Networks." Thesis, Mälardalen University, School of Innovation, Design and Engineering, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-6297.

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In this thesis a novel method for controlling a manipulator with arbitrary number of Degrees of freedom is proposed, the proposed method has the main advantages of two common controllers, the simplicity of PID controller and the robustness and accuracy of adaptive controller. The controller architecture is based on an Artificial Neural Network (ANN) and a PID controller.

The controller has the ability of solving inverse dynamics and inverse kinematics of robot with two separate Artificial Neural Networks. Since the ANN is learning the system parameters by itself the structure of controller can easily be changed to

improve the performance of robot.

The proposed controller can be implemented on a FPGA board to control the robot in real-time or the response of the ANN can be calculated offline and be reconstructed by controller using a lookup table. Error between the desired trajectory path and the path of the robot converges to zero rapidly and as the robot performs its tasks the controller learns the robot parameters and generates better control signal. The performance of controller is tested in simulation and on a real manipulator with satisfactory results.

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8

Göbel, Werner. "3D laser-scanning techniques for two-photon calcium imaging of neural network dynamics in vivo /." Zürich : ETH, 2008. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=17655.

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9

Hammond, Alec Michael. "Machine Learning Methods for Nanophotonic Design, Simulation, and Operation." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7131.

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Interest in nanophotonics continues to grow as integrated optics provides an affordable platform for areas like telecommunications, quantum information processing, and biosensing. Designing and characterizing integrated photonics components and circuits, however, remains a major bottleneck. This is especially true when complex circuits or devices are required to study a particular phenomenon.To address this challenge, this work develops and experimentally validates a novel machine learning design framework for nanophotonic devices that is both practical and intuitive. As case studies, artificial neural networks are trained to model strip waveguides, integrated chirped Bragg gratings, and microring resonators using a small number of simple input and output parameters relevant to designers. Once trained, the models significantly decrease the computational cost relative to traditional design methodologies. To illustrate the power of the new design paradigm, both forward and inverse design tools enabled by the new design paradigm are demonstrated. These tools are directly used to design and fabricate several integrated Bragg grating devices and ring resonator filters. The method's predictions match the experimental measurements well and do not require any post-fabrication training adjustments.
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10

Sheppard, Steven James. "Diagnosis from single photon emission tomography images of the human brain using artificial neural networks." Thesis, University of Warwick, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.307348.

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Книги з теми "Photonic Neural Network"

1

J, Miceli William, Neff John A, Kowel Stephen T, and Society of Photo-optical Instrumentation Engineers., eds. Photonics for computers, neural networks, and memories: 22-24 July 1992, San Diego, California. Bellingham, Wash: SPIE, 1993.

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2

R, Pirich Andrew, and Society of Photo-optical Instrumentation Engineers., eds. Photonic component engineering and applications: 8-9 April 1996, Orlando, Florida. Bellingham, Wash: SPIE, 1996.

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3

L, Horner Joseph, and Society of Photo-optical Instrumentation Engineers., eds. Photonics for processors, neural networks, and memories: 12-15 July 1993, San Diego, California. Bellingham, Wash., USA: SPIE--the International Society for Optical Engineering, 1993.

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4

L, Horner Joseph, Javidi Bahram, Kowel Stephen T, and Society of Photo-optical Instrumentation Engineers., eds. Photonics for processors, neural networks, and memories II: 25-28 July 1994, San Diego, California. Bellingham, Wash., USA: SPIE, 1994.

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5

Sheppard, Steven James. Diagnosis from single photon emission tomography images of the human brain using artificial neural networks. [s.l.]: typescript, 1995.

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6

Brunner, Daniel, Miguel C. Soriano, and Guy Van der Sande. Photonic Reservoir Computing: Optical Recurrent Neural Networks. de Gruyter GmbH, Walter, 2019.

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7

Brunner, Daniel, Miguel C. Soriano, and Guy Van der Sande. Photonic Reservoir Computing: Optical Recurrent Neural Networks. de Gruyter GmbH, Walter, 2019.

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8

Brunner, Daniel, Miguel C. Soriano, and Guy Van der Sande. Photonic Reservoir Computing: Optical Recurrent Neural Networks. de Gruyter GmbH, Walter, 2019.

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9

Vittorio, Massimo De, Luigi Martiradonna, and John Assad. Nanotechnology and Neuroscience: Nano-electronic, Photonic and Mechanical Neuronal Interfacing. Springer, 2014.

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10

Vittorio, Massimo De, Luigi Martiradonna, and John Assad. Nanotechnology and Neuroscience: Nano-electronic, Photonic and Mechanical Neuronal Interfacing. Springer, 2016.

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Частини книг з теми "Photonic Neural Network"

1

Shoop, Barry L. "A Photonic-Based Error Diffusion Neural Network." In Photonic Analog-to-Digital Conversion, 215–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-540-44408-4_8.

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2

Bergeron, Alain, Henri H. Arsenault, Michel Doucet, and Denis Gingras. "Optical Position Converter for Target Tracking and Neural Network." In Applications of Photonic Technology 2, 537–41. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4757-9250-8_86.

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3

Xia, Chengpeng, Yawen Chen, Haibo Zhang, Hao Zhang, and Jigang Wu. "Photonic Computing and Communication for Neural Network Accelerators." In Parallel and Distributed Computing, Applications and Technologies, 121–28. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96772-7_12.

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4

Arsenault, Henri H., and Philippe Gagné. "Optical Neural Network for Rotation Invariant and Parallel Classification of 2-D Images Using Optical Image Compression." In Applications of Photonic Technology 2, 487–91. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4757-9250-8_79.

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5

Sheu, Bing J., and Joongho Choi. "Photonic Neural Networks." In Neural Information Processing and VLSI, 369–96. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4615-2247-8_13.

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6

Granger, André, Tigran Galstyan, and Roger A. Lessard. "Error-Diffusion Binarization for Neural Networks." In Applications of Photonic Technology 2, 527–35. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4757-9250-8_85.

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7

Hennessey, G., H. Leung, and A. Drosopoulos. "Radar Image Modelling and Detection Using Neural Networks." In Applications of Photonic Technology 2, 711–21. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4757-9250-8_108.

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8

Oikonomou, A., M. Kirtas, N. Passalis, G. Mourgias-Alexandris, M. Moralis-Pegios, N. Pleros, and A. Tefas. "A Robust, Quantization-Aware Training Method for Photonic Neural Networks." In Engineering Applications of Neural Networks, 427–38. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08223-8_35.

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9

Meng, Guanghan, Qinrong Zhang, and Na Ji. "High-Speed Neural Imaging with Synaptic Resolution: Bessel Focus Scanning Two-Photon Microscopy and Optical-Sectioning Widefield Microscopy." In Neuromethods, 293–329. New York, NY: Springer US, 2023. http://dx.doi.org/10.1007/978-1-0716-2764-8_10.

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AbstractBrain is composed of complex networks of neurons that work in concert to underlie the animal’s cognition and behavior. Neurons communicate via structures called synapses, which typically require submicron spatial resolution to visualize. To understand the computation of individual neurons as well as neural networks, methods that can monitor neuronal morphology and function in vivo at synaptic spatial resolution and sub-second temporal resolution are required. In this chapter, we discuss the principles and applications of two enabling optical microscopy methods: two-photon fluorescence microscopy equipped with Bessel focus scanning technology and widefield fluorescence microscopy with optical sectioning ability, both of which could be combined with optogenetic stimulation for all optical interrogation of neural circuits. Details on their design and implementation, as well as example applications, are presented.
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10

Antonik, Piotr, Michiel Hermans, Marc Haelterman, and Serge Massar. "Towards Adjustable Signal Generation with Photonic Reservoir Computers." In Artificial Neural Networks and Machine Learning – ICANN 2016, 374–81. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44778-0_44.

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Тези доповідей конференцій з теми "Photonic Neural Network"

1

Shastri, Bhavin J., Volker Sorger, and Nir Rotenberg. "In situ Training of Silicon Photonic Neural Networks: from Classical to Quantum." In CLEO: Science and Innovations. Washington, D.C.: Optica Publishing Group, 2023. http://dx.doi.org/10.1364/cleo_si.2023.sm4j.1.

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Photonic neural networks perform ultrafast inference operations but are trained on slow computers. We highlight on-chip network training enabled by silicon photonics. We introduce quantum photonic neural networks and discuss the role of weak nonlinearities.
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2

Dordoev, Sagynbek, Askar A. Kutanov, and Baktybek D. Abdrisaev. "Holographic disk-based photonic neural network." In Photonic Neural Networks. SPIE, 1993. http://dx.doi.org/10.1117/12.983203.

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3

Psaltis, Demetri. "Optical Neural Computers." In Photonic Switching. Washington, D.C.: Optica Publishing Group, 1987. http://dx.doi.org/10.1364/phs.1987.wb3.

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The development of optical computers of any type is based on the notion that semiconductor technology imposes limitations in the performance of current computers which prevent them from being effectively used for the solution of a class of interesting computational problems. If optics is used instead, these limitations will be lifted and we will therefore be able to now solve these interesting problems. Global connectivity is perhaps the most distinctive feature of optics vis-a-vis semiconductor technology, and the development of optical neural computers can be viewed as an attempt to exploit this feature. In a neural network each elementary computational unit, the neuron, directly communicates to thousands of others, while in electronic computers each gate is typically connected to only two or three gates. With optics it is feasible to realize the dense connectivity that is evident in neural networks. This provides the impetus for examining neural network models of computation to get ideas about how to build optical computers whose performance is clearly better than their electronic counterparts.
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4

Natarajan, Sanjay S., and David P. Casasent. "Piecewise quadratic optical neural network." In Photonic Neural Networks. SPIE, 1993. http://dx.doi.org/10.1117/12.983199.

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5

Li, Chunfei, Shutian Liu, Jie Wu, Wenlu Wang, and Ruibo Wang. "Feature-enhanced optical interpattern associative neural network." In Photonic Neural Networks. SPIE, 1993. http://dx.doi.org/10.1117/12.983194.

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6

Kyriakakis, C., Z. Karim, A. R. Tanguay, R. F. Cartland, A. Madhukar, S. Piazzolla, B. K. Jenkins, C. B. Kuznia, A. A. Sawchuk, and C. von der Malsburg. "Photonic Implementations of Neural Networks." In Optical Computing. Washington, D.C.: Optica Publishing Group, 1995. http://dx.doi.org/10.1364/optcomp.1995.otub1.

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Several broad classes of neural networks comprise distributed, nonlinear, dynamical systems in which large numbers of relatively simple processing elements (neuron units) are densely interconnected. The interconnections are often configured such that the interconnection weights are adaptive and contain the learned memories and behaviors of the system. Advanced optical interconnection techniques are being developed that can potentially be used in conjunction with optoelectronic neuron units to implement photonic neural-like computational modules (e.g., Fig. 1) with relatively large array sizes (105 to 106 neuron units) and a high degree of connectivity (fan-outs and fan-ins of 104 to 106, with 109 to 1012 total interconnections). A key open question is whether the high bandwidths (potentially 100 MHz or more) available from hybrid optoelectronic spatial light modulators (SLMs) can be effectively combined with such high density volume holographic optical interconnections (dynamically recorded in photorefractive materials) to provide enhanced computational throughput capacity as well as complex neural network simulation capability. A second key open question is whether advanced electronic/photonic packaging technologies can provide capability for system-level integration of highly compact multichip modules that exhibit both local (multi-plane) and global interconnections (Fig. 2).
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7

Chou, Hung, Andrew A. Kostrzewski, Shudong Wu, Freddie S. Lin, and Thomas T. Lu. "Performance evaluation of a holographic optical neural network system." In Photonic Neural Networks. SPIE, 1993. http://dx.doi.org/10.1117/12.983187.

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8

Barua, Susamma. "Optical and systolic implementation of an artificial neural network." In Photonic Neural Networks. SPIE, 1993. http://dx.doi.org/10.1117/12.983197.

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9

Henshaw, Philip D., and Steven A. Lis. "Experimental implementation of an optical neural network scalable to very large size." In Photonic Neural Networks. SPIE, 1993. http://dx.doi.org/10.1117/12.983190.

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10

Shiflett, Kyle, Dylan Wright, Avinash Karanth, and Ahmed Louri. "PIXEL: Photonic Neural Network Accelerator." In 2020 IEEE International Symposium on High Performance Computer Architecture (HPCA). IEEE, 2020. http://dx.doi.org/10.1109/hpca47549.2020.00046.

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