Bibliografías temáticas / Photonic computing

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Literatura académica sobre el tema "Photonic computing"

Autor: Grafiati

Publicado: 15 de febrero de 2025

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Artículos de revistas sobre el tema "Photonic computing"

Bocker, Richard P. "Photonic computing". Applied Optics 25, n.º 18 (15 de septiembre de 1986): 3019. http://dx.doi.org/10.1364/ao.25.003019.

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Sun, Haoyang, Qifeng Qiao, Qingze Guan y Guangya Zhou. "Silicon Photonic Phase Shifters and Their Applications: A Review". Micromachines 13, n.º 9 (12 de septiembre de 2022): 1509. http://dx.doi.org/10.3390/mi13091509.

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With the development of silicon photonics, dense photonic integrated circuits play a significant role in applications such as light detection and ranging systems, photonic computing accelerators, miniaturized spectrometers, and so on. Recently, extensive research work has been carried out on the phase shifter, which acts as the fundamental building block in the photonic integrated circuit. In this review, we overview different types of silicon photonic phase shifters, including micro-electro-mechanical systems (MEMS), thermo-optics, and free-carrier depletion types, highlighting the MEMS-based ones. The major working principles of these phase shifters are introduced and analyzed. Additionally, the related works are summarized and compared. Moreover, some emerging applications utilizing phase shifters are introduced, such as neuromorphic computing systems, photonic accelerators, multi-purpose processing cores, etc. Finally, a discussion on each kind of phase shifter is given based on the figures of merit.

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Xu, Zhihao, Tiankuang Zhou, Muzhou Ma, ChenChen Deng, Qionghai Dai y Lu Fang. "Large-scale photonic chiplet Taichi empowers 160-TOPS/W artificial general intelligence". Science 384, n.º 6692 (12 de abril de 2024): 202–9. http://dx.doi.org/10.1126/science.adl1203.

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The pursuit of artificial general intelligence (AGI) continuously demands higher computing performance. Despite the superior processing speed and efficiency of integrated photonic circuits, their capacity and scalability are restricted by unavoidable errors, such that only simple tasks and shallow models are realized. To support modern AGIs, we designed Taichi—large-scale photonic chiplets based on an integrated diffractive-interference hybrid design and a general distributed computing architecture that has millions-of-neurons capability with 160–tera-operations per second per watt (TOPS/W) energy efficiency. Taichi experimentally achieved on-chip 1000-category–level classification (testing at 91.89% accuracy in the 1623-category Omniglot dataset) and high-fidelity artificial intelligence–generated content with up to two orders of magnitude of improvement in efficiency. Taichi paves the way for large-scale photonic computing and advanced tasks, further exploiting the flexibility and potential of photonics for modern AGI.

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Tanida, Jun y Yusuke Ogura. "Photonic DNA computing". Review of Laser Engineering 33, Supplement (2005): 239–40. http://dx.doi.org/10.2184/lsj.33.239.

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Kutluyarov, Ruslan V., Aida G. Zakoyan, Grigory S. Voronkov, Elizaveta P. Grakhova y Muhammad A. Butt. "Neuromorphic Photonics Circuits: Contemporary Review". Nanomaterials 13, n.º 24 (14 de diciembre de 2023): 3139. http://dx.doi.org/10.3390/nano13243139.

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Neuromorphic photonics is a cutting-edge fusion of neuroscience-inspired computing and photonics technology to overcome the constraints of conventional computing architectures. Its significance lies in the potential to transform information processing by mimicking the parallelism and efficiency of the human brain. Using optics and photonics principles, neuromorphic devices can execute intricate computations swiftly and with impressive energy efficiency. This innovation holds promise for advancing artificial intelligence and machine learning while addressing the limitations of traditional silicon-based computing. Neuromorphic photonics could herald a new era of computing that is more potent and draws inspiration from cognitive processes, leading to advancements in robotics, pattern recognition, and advanced data processing. This paper reviews the recent developments in neuromorphic photonic integrated circuits, applications, and current challenges.

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Li, Jiang, Chaoyue Liu, Haitao Chen, Jingshu Guo, Ming Zhang y Daoxin Dai. "Hybrid silicon photonic devices with two-dimensional materials". Nanophotonics 9, n.º 8 (14 de mayo de 2020): 2295–314. http://dx.doi.org/10.1515/nanoph-2020-0093.

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AbstractSilicon photonics is becoming more and more attractive in the applications of optical interconnections, optical computing, and optical sensing. Although various silicon photonic devices have been developed rapidly, it is still not easy to realize active photonic devices and circuits with silicon alone due to the intrinsic limitations of silicon. In recent years, two-dimensional (2D) materials have attracted extensive attentions due to their unique properties in electronics and photonics. 2D materials can be easily transferred onto silicon and thus provide a promising approach for realizing active photonic devices on silicon. In this paper, we give a review on recent progresses towards hybrid silicon photonics devices with 2D materials, including two parts. One is silicon-based photodetectors with 2D materials for the wavelength-bands from ultraviolet (UV) to mid-infrared (MIR). The other is silicon photonic switches/modulators with 2D materials, including high-speed electro-optical modulators, high-efficiency thermal-optical switches and low-threshold all-optical modulators, etc. These hybrid silicon photonic devices with 2D materials devices provide an alternative way for the realization of multifunctional silicon photonic integrated circuits in the future.

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Dong, Bowei, Frank Brückerhoff-Plückelmann, Lennart Meyer, Jelle Dijkstra, Ivonne Bente, Daniel Wendland, Akhil Varri et al. "Partial coherence enhances parallelized photonic computing". Nature 632, n.º 8023 (31 de julio de 2024): 55–62. http://dx.doi.org/10.1038/s41586-024-07590-y.

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AbstractAdvancements in optical coherence control1–5 have unlocked many cutting-edge applications, including long-haul communication, light detection and ranging (LiDAR) and optical coherence tomography6–8. Prevailing wisdom suggests that using more coherent light sources leads to enhanced system performance and device functionalities9–11. Our study introduces a photonic convolutional processing system that takes advantage of partially coherent light to boost computing parallelism without substantially sacrificing accuracy, potentially enabling larger-size photonic tensor cores. The reduction of the degree of coherence optimizes bandwidth use in the photonic convolutional processing system. This breakthrough challenges the traditional belief that coherence is essential or even advantageous in integrated photonic accelerators, thereby enabling the use of light sources with less rigorous feedback control and thermal-management requirements for high-throughput photonic computing. Here we demonstrate such a system in two photonic platforms for computing applications: a photonic tensor core using phase-change-material photonic memories that delivers parallel convolution operations to classify the gaits of ten patients with Parkinson’s disease with 92.2% accuracy (92.7% theoretically) and a silicon photonic tensor core with embedded electro-absorption modulators (EAMs) to facilitate 0.108 tera operations per second (TOPS) convolutional processing for classifying the Modified National Institute of Standards and Technology (MNIST) handwritten digits dataset with 92.4% accuracy (95.0% theoretically).

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Chetan, Anjna. "Integration of Photonic Circuits in Electronics for Enhanced Data Processing and Transfer". Journal for Research in Applied Sciences and Biotechnology 1, n.º 2 (30 de junio de 2022): 83–89. http://dx.doi.org/10.55544/jrasb.1.2.9.

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The rapid growth of data-intensive applications, such as artificial intelligence (AI), big data analytics, and cloud computing, has highlighted the limitations of traditional electronic circuits, particularly in terms of data transfer rates, processing power, and energy efficiency. This study explores the integration of photonic circuits with electronic systems as a viable solution to these challenges. By leveraging the speed and efficiency of photons for data transmission, photonic circuits promise substantial improvements over conventional electronic circuits. The research employs a mixed-method approach, combining experimental analysis with a comprehensive literature review. Experimental results demonstrate that hybrid photonic-electronic circuits can achieve up to ten times faster data processing speeds, a 30% reduction in power consumption, increased bandwidth, and reduced latency compared to traditional electronic systems. These advancements address key issues such as resistive losses and heat generation, offering enhanced performance for high-demand applications. However, challenges related to signal conversion and thermal management persist. Future research is needed to refine photonic-electronic integration and explore advanced technologies, including quantum photonics, to further enhance data processing capabilities. Overall, the study highlights the significant potential of photonic circuits to revolutionize data systems, providing a path towards next-generation computing technologies.

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Argyris, Apostolos. "Photonic neuromorphic technologies in optical communications". Nanophotonics 11, n.º 5 (19 de enero de 2022): 897–916. http://dx.doi.org/10.1515/nanoph-2021-0578.

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Abstract Machine learning (ML) and neuromorphic computing have been enforcing problem-solving in many applications. Such approaches found fertile ground in optical communications, a technological field that is very demanding in terms of computational speed and complexity. The latest breakthroughs are strongly supported by advanced signal processing, implemented in the digital domain. Algorithms of different levels of complexity aim at improving data recovery, expanding the reach of transmission, validating the integrity of the optical network operation, and monitoring data transfer faults. Lately, the concept of reservoir computing (RC) inspired hardware implementations in photonics that may offer revolutionary solutions in this field. In a brief introduction, I discuss some of the established digital signal processing (DSP) techniques and some new approaches based on ML and neural network (NN) architectures. In the main part, I review the latest neuromorphic computing proposals that specifically apply to photonic hardware and give new perspectives on addressing signal processing in optical communications. I discuss the fundamental topologies in photonic feed-forward and recurrent network implementations. Finally, I review the photonic topologies that were initially tested for channel equalization benchmark tasks, and then in fiber transmission systems, for optical header recognition, data recovery, and modulation format identification.

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Sun, Shuai, Mario Miscuglio, Xiaoxuan Ma, Zhizhen Ma, Chen Shen, Engin Kayraklioglu, Jeffery Anderson, Tarek El Ghazawi y Volker J. Sorger. "Induced homomorphism: Kirchhoff’s law in photonics". Nanophotonics 10, n.º 6 (22 de marzo de 2021): 1711–21. http://dx.doi.org/10.1515/nanoph-2020-0655.

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Abstract When solving, modeling or reasoning about complex problems, it is usually convenient to use the knowledge of a parallel physical system for representing it. This is the case of lumped-circuit abstraction, which can be used for representing mechanical and acoustic systems, thermal and heat-diffusion problems and in general partial differential equations. Integrated photonic platforms hold the prospective to perform signal processing and analog computing inherently, by mapping into hardware specific operations which relies on the wave-nature of their signals, without trusting on logic gates and digital states like electronics. Here, we argue that in absence of a straightforward parallelism a homomorphism can be induced. We introduce a photonic platform capable of mimicking Kirchhoff’s law in photonics and used as node of a finite difference mesh for solving partial differential equation using monochromatic light in the telecommunication wavelength. Our approach experimentally demonstrates an arbitrary set of boundary conditions, generating a one-shot discrete solution of a Laplace partial differential equation, with an accuracy above 95% with respect to commercial solvers. Our photonic engine can provide a route to achieve chip-scale, fast (10 s of ps), and integrable reprogrammable accelerators for the next generation hybrid high-performance computing. Summary A photonic integrated platform which can mimic Kirchhoff’s law in photonics is used for approximately solve partial differential equations noniteratively using light, with high throughput and low-energy levels.

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Tesis sobre el tema "Photonic computing"

Cao, Yameng. "Semiconductor light sources for photonic quantum computing". Thesis, Imperial College London, 2015. http://hdl.handle.net/10044/1/56619.

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The isolation of qubits from decoherence is crucial to the prospect of building revolutionary quantum devices. This work is devoted to an optical study of the decoherence on spin qubits in self-assembled quantum dots. This thesis contributes towards a complete understanding of quantum decoherence, of which highlighted discoveries include bypassing the spectral diffusion in neutral quantum dot emission lines; observing for the first time the self-polarization phenomenon of nuclear spins, via the resonance-locking effect on a negatively charged quantum dot; and revealing the limiting factors on hole spin dephasing, by measuring polarization correlations on a positively charged quantum dot. Three studies were conducted using two different spectroscopy techniques. For the first study, the spectral diffusion of emission line due to random electrostatic fluctuations was revealed, by scanning a neutral quantum dot transition across the laser resonance. Exciting the quantum dot resonantly bypassed this problem, paving the way for an on-demand antibunched source that generates narrow-band photons. For the second study, evidences supporting the spontaneous self-polarization of nuclear spins were observed for the first time, since it was predicted nearly four decades ago by M. Dyankonov and V.I. Perel. The self-polarization phenomenon is a remarkable demonstration of dynamic nuclear spin polarization since it manifests without the ground state electron being spin-polarized. In the last study, factors limiting the hole spin lifetime was inferred from measuring polarization correlation of successively emitted photons from a positively charged quantum dot. Evidences support a strong dependence on the carrier repopulation rate and the single electron spin dephasing in the upper state, due to the Overhauser field. In combination with the observation of spontaneous nuclear polarization, this opens the possibility of an electron spin sensor, which can indirectly probe the nuclear field.

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Birchall, Patrick Matthew. "Fundamental advantages and practicalities of quantum-photonic metrology and computing". Thesis, University of Bristol, 2018. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.752791.

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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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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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Denis-Le, Coarer Florian. "Neuromorphic computing using nonlinear ring resonators on a Silicon photonic chip". Electronic Thesis or Diss., CentraleSupélec, 2020. http://www.theses.fr/2020CSUP0001.

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Avec les volumes exponentiels de données numériques générées chaque jour, un besoin de traitement des données en temps réel et économe en énergie s'est fait sentir. Ces défis ont motivé la recherche sur le traitement non conventionnel de l'information. Parmi les techniques existantes, l'apprentissage machine est un paradigme très efficace de l'informatique cognitive. Il fournit, au travers de nombreuses implémentations dont celle des réseaux de neurones artificiels, un ensemble de techniques pour apprendre à un ordinateur ou un système physique à effectuer des tâches complexes, telles que la classification, la reconnaissance de formes ou la génération de signaux. Le reservoir computing a été proposé il y a une dizaine d'années pour simplifier la procédure d’entraînement du réseau de neurones artificiels. En effet, le réseau est maintenu fixe et seules les connexions entre la couche de lecture et la sortie sont entraînées par une simple régression linéaire. L'architecture interne d’un reservoir computer permet des implémentations au niveau physique, et plusieurs implémentations ont été proposées sur différentes plateformes technologiques, dont les dispositifs photoniques. Le reservoir computing sur circuits intégrés optiques est un candidat très prometteur pour relever ces défis. L’objectif de ce travail de thèse a été de proposer trois architectures différentes de réservoir intégré basées sur l’utilisation des micro-anneaux résonnants. Nous en avons numériquement étudié les performances et mis en évidence des vitesses de traitement de données pouvant atteindre plusieurs dizaines de Gigabit par seconde avec des consommations énergétiques de quelques milliwatt
With the exponential volumes of digital data generated every day, there is a need for real-time, energy-efficient data processing. These challenges have motivated research on unconventional information processing. Among the existing techniques, machine learning is a very effective paradigm of cognitive computing. It provides, through many implementations including that of artificial neural networks, a set of techniques to teach a computer or physical system to perform complex tasks, such as classification, pattern recognition or signal generation. Reservoir computing was proposed about ten years ago to simplify the procedure for training the artificial neural network. Indeed, the network is kept fixed and only the connections between the reading layer and the output are driven by a simple linear regression. The internal architecture of a reservoir computer allows physical implementations, and several implementations have been proposed on different technological platforms, including photonic devices. On-chip reservoir computing is a very promising candidate to meet these challenges. The objective of this thesis work was to propose three different integrated reservoir architectures based on the use of resonant micro-rings. We have digitally studied its performance and highlighted data processing speeds of up to several tens of Gigabits per second with energy consumption of a few milliwatts

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Mwamsojo, Nickson. "Neuromorphic photonic systems for information processing". Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAS002.

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Par une utilisation performante de nombreux algorithmes dont les réseaux neuronaux, l'intelligence artificielle révolutionne le développement de la société numérique. Néanmoins, la tendance actuelle dépasse les limites prédites par la loi de Moore et celle de Koomey, ce qui implique des limitations éventuelles des implémentations numériques de ces systèmes. Pour répondre plus efficacement aux besoins calculatoires spécifiques de cette révolution, des systèmes physiques innovants tentent en amont d'apporter des solutions, nommées "neuro-morphiques" puisqu'elles imitent le fonctionnement des cerveaux biologiques. Les systèmes existants sont basés sur des techniques dites de "Reservoir Computing" ou "coherent Ising Machine." Leurs versions photoniques, ont permis de démontrer l'intérêt de ces techniques notamment pour la reconnaissance vocale avec un état de l'art en 2017 attestant de bonnes performances en termes de reconnaissance à un rythme d'1 million de mots par seconde. Nous proposons dans un premier temps une technique d'ajustement automatique des hyperparamètres pour le "Reservoir Computing", accompagnée d'une étude théorique de convergence. Nous proposons ensuite une solution au problème de la détection précoce de la maladie d'Alzheimer de type "Reservoir Computing" optoélectronique. En plus des taux de classifications obtenus meilleurs que l'état de l'art, une étude complète du compromis coût énergétique performance démontre la validité de cette approche. Enfin, le problème de la restauration d'image par maximum de vraisemblance est abordé à l'aide d'une implémentation optoélectronique appropriée de type "coherent Ising Machine"
Artificial Intelligence has revolutionized the scientific community thanks to the advent of a robust computation workforce and Artificial Neural Neural Networks. However, the current implementation trends introduce a rapidly growing demand for computational power surpassing the rates and limitations of Moore's and Koomey's Laws, which implies an eventual efficiency barricade. To respond to these demands, bio-inspired techniques, known as 'neuro-morphic' systems, are proposed using physical devices. Of these systems, we focus on 'Reservoir Computing' and 'Coherent Ising Machines' in our works.Reservoir Computing, for instance, demonstrated its computation power such as the state-of-the-art performance of up to 1 million words per second using photonic hardware in 2017. We propose an automatic hyperparameter tuning algorithm for Reservoir Computing and give a theoretical study of its convergence. Moreover, we propose Reservoir Computing for early-stage Alzheimer's disease detection with a thorough assessment of the energy costs versus performance compromise. Finally, we confront the noisy image restoration problem by maximum a posteriori using an optoelectronic implementation of a Coherent Ising Machine

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Alipour, Motaallem Seyed Payam. "Reconfigurable integrated photonic circuits on silicon". Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/51792.

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Integrated optics as a platform for signal processing offers significant benefits such as large bandwidth, low loss, and a potentially high degree of reconfigurability. Silicon (Si) has unique advantages as a material platform for integration, as well as properties such as a strong thermo-optic mechanism that allows for the realization of highly reconfigurable photonic systems. Chapter 1 is devoted to the discussion of these advantages, and Chapter 2 provides the theoretical background for the analysis of integrated Si-photonic devices. The thermo-optic property of Si, while proving extremely useful in facilitating reconfiguration, can turn into a nuisance when there is a need for thermally stable devices on the photonic chip. Chapter 3 presents a technique for resolving this issue without relying on a dynamic temperature stabilization process. Temperature-insensitive (or “athermal”) Si microdisk resonators with low optical loss are realized by using a polymer overlayer whose thermo-optic property is opposite to that of Si, and TiO2 is introduced as an alternative to polymer to deal with potential CMOS-compatibility issues. Chapter 4 demonstrates an ultra-compact, low-loss, fully reconfigurable, and high-finesse integrated photonic filter implemented on a Si chip, which can be used for RF-photonic as well as purely optical signal processing purposes. A novel, thermally reconfigurable reflection suppressor is presented in Chapter 5 for on-chip feedback elimination which can be critical for mitigating spurious interferences and protecting lasers from disturbance. Chapter 6 demonstrates a novel device for on-chip control of optical fiber polarization. Chapter 7 deals with select issues in the implementation of Si integrated photonic circuits. Chapter 8 concludes the dissertation.

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Mohamed, Abdalla Mohab Sameh. "Reservoir computing in lithium niobate on insulator platforms". Electronic Thesis or Diss., Ecully, Ecole centrale de Lyon, 2024. http://www.theses.fr/2024ECDL0051.

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Cette étude concerne le calcul par réservoir à retard temporel, en anglais Time-Delay Reservoir Computing (TDRC) dans les plateformes de photonique intégré, en particulier la plateforme Lithium Niobate On Insulator (LNOI). Nous proposons une nouvelle architecture intégrée « tout optique », avec seulement un déphaseur comme paramètre modifiable pouvant atteindre de bonnes performances sur plusieurs tâches de référence de calcul par réservoir. Nous étudions également l'espace de conception de cette architecture et le fonctionnement asynchrone du TDRC, qui s'écarte du cadre plus courant consistant à envisager les ordinateurs TDRC comme des réseaux. En outre, nous suggérons d'exploiter le schéma tout optique pour se passer du masque d'entrée, ce qui permet de contourner la conversion Optique/Electronique/Optique (O/E/O), souvent nécessaire pour appliquer le masque dans les architectures TDRC. Dans des travaux futurs, cela pourra permettre le traitement de signaux entrants en temps réel, éventuellement pour des applications de télécommunication de pointe. Les effets de la lecture électronique de sortie sur cette architecture sont également étudiés. Aussi, nous suggérons d'utiliser la corrélation de Pearson comme une métrique nous permettant de concevoir un réservoir capable de traiter plusieurs tâches en même temps sur le même signal entrant (et éventuellement sur des signaux dans des canaux différents). Les premiers travaux expérimentaux menés à l'université RMIT sont également présentés. Par ces travaux, nous voulons étudier la performance de ces nouvelles architectures TDRC tout en ayant minimisant la complexité du matériel photonique. Pour cela on s’appuiera principalement sur les faibles pertes du LNOI qui permettent l'intégration du guide d'onde de rétroaction, et en utilisant uniquement l'interférence et la conversion d'intensité à la sortie (par le biais d'un photodétecteur) en tant que non-linéarité. Cela constitue une base sur laquelle pourront s’appuyer de futurs travaux étudiant les gains de performance lorsque des non-linéarités supplémentaires sont prises en compte (telles que celles de la plateforme LNOI) et lorsque la complexité globale du système augmente par l'introduction d'un plus grand nombre de paramètres. Ces travaux portent donc sur l'exploration d'une approche informatique non conventionnelle particulière (TDRC), utilisant une technologie particulière (la photonique intégrée), sur une plateforme particulière (LNOI). Ces travaux s'appuient sur l'intérêt croissant pour l'informatique non conventionnelle puisqu'il a été démontré au fil des ans que les ordinateurs numériques ne peuvent plus être une solution unique, en particulier pour les applications émergentes telles que l'intelligence artificielle (IA). Le paysage futur de l'informatique englobera probablement une grande variété de paradigmes informatiques, d'architectures et de hardware, afin de répondre aux besoins d'applications spécialisées croissantes, tout en coexistant avec les ordinateurs numériques qui restent - du moins pour l'instant - mieux adaptés à l'informatique à usage général
This work concerns time-delay reservoir computing (TDRC) in integrated photonic platforms, specifically the Lithium Niobate on Insulator (LNOI) platform. We propose a novel all-optical integrated architecture, which has only one tunable parameter in the form of a phase-shifter, and which can achieve good performance on several reservoir computing benchmark tasks. We also investigate the design space of this architecture and the asynchronous operation, which represents a departure from the more common framework of envisioning time-delay reservoir computers as networks in the stricter sense. Additionally, we suggest to leverage the all-optical scheme to dispense with the input mask, which allows the bypassing of an O/E/O conversion, often necessary to apply the mask in TDRC architectures. In future work, this can allow the processing of real-time incoming signals, possibly for telecom/edge applications. The effects of the output electronic readout on this architecture are also investigated. Furthermore, it is suggested to use the Pearson correlation as a simple way to design a reservoir which can handle multiple tasks at the same time, on the same incoming signal (and possibly on signals in different channels). Initial experimental work carried out at RMIT University is also reported. The unifying theme of this work is to investigate the performance possibilities with minimum photonic hardware requirements, relying mainly on LNOI’s low losses which enables the integration of the feedback waveguide, and using only interference and subsequent intensity conversion (through a photodetector) as the nonlinearity. This provides a base for future work to compare against in terms of performance gains when additional nonlinearities are considered (such as those of the LNOI platform), and when overall system complexity is increased by means of introducing more tunable parameters. Thus, the scope of this work is about the exploration of one particular unconventional computing approach (reservoir computing), using one particular technology (photonics), on one particular platform (lithium niobate on insulator). This work builds on the increasing interest of exploring unconventional computing, since it has been shown over the years that digital computers can no longer be a `one-size-fits-all', especially for emerging applications like artificial intelligence (AI). The future landscape of computing will likely encompass a rich variety of computing paradigms, architectures, and hardware, to meet the needs of rising specialized applications, and all in coexistence with digital computers which remain --- at least for now --- better suited for general-purpose computing

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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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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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Thraskias, Christos A., Eythimios N. Lallas, Niels Neumann, Laurent Schares, Bert J. Offrein, Ronny Henker, Dirk Plettemeier, Frank Ellinger, Juerg Leuthold y Ioannis Tomkos. "Survey of Photonic and Plasmonic Interconnect Technologies for Intra-Datacenter and High-Performance Computing Communications". Institute of Electrical and Electronics Engineers (IEEE), 2018. https://tud.qucosa.de/id/qucosa%3A35391.

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Large scale data centers (DC) and high performance computing (HPC) systems require more and more computing power at higher energy efficiency. They are already consuming megawatts of power, and a linear extrapolation of trends reveals that they may eventually lead to unrealistic power consumption scenarios in order to satisfy future requirements (e.g., Exascale computing). Conventional complementary metal oxide semiconductor (CMOS)-based electronic interconnects are not expected to keep up with the envisioned future board-to-board and chip-to-chip (within multi-chip-modules) interconnect requirements because of bandwidth-density and power-consumption limitations. However, low-power and high-speed optics-based interconnects are emerging as alternatives for DC and HPC communications; they offer unique opportunities for continued energy-efficiency and bandwidth-density improvements, although cost is a challenge at the shortest length scales. Plasmonics-based interconnects on the other hand, due to their extremely small size, offer another interesting solution for further scaling operational speed and energy efficiency. At the device-level, CMOS compatibility is also an important issue, since ultimately photonics or plasmonics will have to be co-integrated with electronics. In this paper, we survey the available literature and compare the aforementioned interconnect technologies, with respect to their suitability for high-speed and energy-efficient on-chip and offchip communications. This paper refers to relatively short links with potential applications in the following interconnect distance hierarchy: local group of racks, board to board, module to module, chip to chip, and on chip connections. We compare different interconnect device modules, including low-energy output devices (such as lasers, modulators, and LEDs), photodetectors, passive devices (i.e., waveguides and couplers) and electrical circuitry (such as laserdiode drivers, modulator drivers, transimpedance, and limiting amplifiers). We show that photonic technologies have the potential to meet the requirements for selected HPC and DC applications in a shorter term. We also present that plasmonic interconnect modules could offer ultra-compact active areas, leading to high integration bandwidth densities, and low device capacitances allowing for ultra-high bandwidth operation that would satisfy the application requirements further into the future.

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Marquez, Alfonzo Bicky. "Reservoir computing photonique et méthodes non-linéaires de représentation de signaux complexes : Application à la prédiction de séries temporelles". Thesis, Bourgogne Franche-Comté, 2018. http://www.theses.fr/2018UBFCD042/document.

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Les réseaux de neurones artificiels constituent des systèmes alternatifs pour effectuer des calculs complexes, ainsi que pour contribuer à l'étude des systèmes neuronaux biologiques. Ils sont capables de résoudre des problèmes complexes, tel que la prédiction de signaux chaotiques, avec des performances à l'état de l'art. Cependant, la compréhension du fonctionnement des réseaux de neurones dans la résolution de problèmes comme la prédiction reste vague ; l'analogie avec une boîte-noire est souvent employée. En combinant la théorie des systèmes dynamiques non linéaires avec celle de l'apprentissage automatique (Machine Learning), nous avons développé un nouveau concept décrivant à la fois le fonctionnement des réseaux neuronaux ainsi que les mécanismes à l'œuvre dans leurs capacités de prédiction. Grâce à ce concept, nous avons pu imaginer un processeur neuronal hybride composé d'un réseaux de neurones et d'une mémoire externe. Nous avons également identifié les mécanismes basés sur la synchronisation spatio-temporelle avec lesquels des réseaux neuronaux aléatoires récurrents peuvent effectivement fonctionner, au-delà de leurs états de point fixe habituellement utilisés. Cette synchronisation a entre autre pour effet de réduire l'impact de la dynamique régulière spontanée sur la performance du système. Enfin, nous avons construit physiquement un réseau récurrent à retard dans un montage électro-optique basé sur le système dynamique d'Ikeda. Celui-ci a dans un premier temps été étudié dans le contexte de la dynamique non-linéaire afin d'en explorer certaines propriétés, puis nous l'avons utilisé pour implémenter un processeur neuromorphique dédié à la prédiction de signaux chaotiques
Artificial neural networks are systems prominently used in computation and investigations of biological neural systems. They provide state-of-the-art performance in challenging problems like the prediction of chaotic signals. Yet, the understanding of how neural networks actually solve problems like prediction remains vague; the black-box analogy is often employed. Merging nonlinear dynamical systems theory with machine learning, we develop a new concept which describes neural networks and prediction within the same framework. Taking profit of the obtained insight, we a-priori design a hybrid computer, which extends a neural network by an external memory. Furthermore, we identify mechanisms based on spatio-temporal synchronization with which random recurrent neural networks operated beyond their fixed point could reduce the negative impact of regular spontaneous dynamics on their computational performance. Finally, we build a recurrent delay network in an electro-optical setup inspired by the Ikeda system, which at first is investigated in a nonlinear dynamics framework. We then implement a neuromorphic processor dedicated to a prediction task

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Libros sobre el tema "Photonic computing"

Brunner, Daniel, Miguel C. Soriano y Guy Van der Sande, eds. Photonic Reservoir Computing. Berlin, Boston: De Gruyter, 2019. http://dx.doi.org/10.1515/9783110583496.

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Nicolescu, Gabriela, Sébastien Le Beux y Mahdi Nikdast. Photonic Interconnects for Computing Systems. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003339076.

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P, Hotaling Steven, Pirich Andrew R y Society of Photo-optical Instrumentation Engineers., eds. Photonic quantum computing: 23-24 April 1997, Orlando, Florida. Bellingham, Wash., USA: SPIE, 1997.

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P, Hotaling Steven, Pirich Andrew R y Society of Photo-optical Instrumentation Engineers., eds. Photonic quantum computing II: 15-16 April 1998, Orlando, Florida. Bellingham, Wash., USA: SPIE, 1998.

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Wang, Howard. Photonic Switches and Networks for High-Performance Computing and Data Centers. [New York, N.Y.?]: [publisher not identified], 2015.

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1966-, Iftekharuddin Khan M., Awwal Abdul A. S y Society of Photo-optical Instrumentation Engineers., eds. Photonic devices and algorithms for computing: 22-23 July 1999, Denver, Colorado. Bellingham, Wash., USA: SPIE, 1999.

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1966-, Iftekharuddin Khan M., Awwal Abdul A. S y Society of Photo-optical Instrumentation Engineers., eds. Photonic devices and algorithms for computing II: 2-3 August 2000, San diego, USA. Bellingham, Wash., USA: SPIE, 2000.

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1966-, Iftekharuddin Khan M., Awwal Abdul A. S y Society of Photo-optical Instrumentation Engineers., eds. Photonic devices and algorithms for computing III: 29-30 July, 2001, San Diego, USA. Bellingham, Wash: SPIE, 2001.

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1966-, Iftekharuddin Khan M., Awwal Abdul A. S y Society of Photo-optical Instrumentation Engineers., eds. Photonic devices and algorithms for computing VI: 2-3 August, 2004, Denver, Colorado, USA. Bellingham, Wash: SPIE, 2004.

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1966-, Iftekharuddin Khan M., Awwal Abdul A. S, Society of Photo-optical Instrumentation Engineers. y Boeing Company, eds. Photonic devices and algorithms for computing IV: 8-9 July, 2002, Seattle, Washington, USA. Bellingham, Washington: SPIE, 2002.

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Capítulos de libros sobre el tema "Photonic computing"

Binh, Le Nguyen. "Photonic Computing Processors". En Photonic Signal Processing, 107–66. Second edition. | Boca Raton : Taylor & Francis, a CRC title, part of the Taylor & Francis imprint, a member of the Taylor & Francis Group, the academic division of T&F Informa, plc, [2019]: CRC Press, 2019. http://dx.doi.org/10.1201/9780429436994-4.

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Chaurasiya, Rohit y Devanshi Arora. "Photonic Quantum Computing". En Quantum and Blockchain for Modern Computing Systems: Vision and Advancements, 127–56. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04613-1_4.

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Easttom, Chuck. "Photonic Quantum Computing". En Hardware for Quantum Computing, 31–48. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-66477-9_3.

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Brunner, Daniel, Piotr Antonik y Xavier Porte. "1. Introduction to novel photonic computing". En Photonic Reservoir Computing, editado por Daniel Brunner, Miguel C. Soriano y Guy Van der Sande, 1–32. Berlin, Boston: De Gruyter, 2019. http://dx.doi.org/10.1515/9783110583496-001.

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Ortín, Silvia, Luis Pesquera, Guy Van der Sande y Miguel C. Soriano. "5. Time delay systems for reservoir computing". En Photonic Reservoir Computing, editado por Daniel Brunner, Miguel C. Soriano y Guy Van der Sande, 117–52. Berlin, Boston: De Gruyter, 2019. http://dx.doi.org/10.1515/9783110583496-005.

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Dambre, Joni. "2. Information processing and computation with photonic reservoir systems". En Photonic Reservoir Computing, editado por Daniel Brunner, Miguel C. Soriano y Guy Van der Sande, 33–52. Berlin, Boston: De Gruyter, 2019. http://dx.doi.org/10.1515/9783110583496-002.

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Katumba, Andrew, Matthias Freiberger, Floris Laporte, Alessio Lugnan, Stijn Sackesyn, Chonghuai Ma, Joni Dambre y Peter Bienstman. "3. Integrated on-chip reservoirs". En Photonic Reservoir Computing, editado por Daniel Brunner, Miguel C. Soriano y Guy Van der Sande, 53–82. Berlin, Boston: De Gruyter, 2019. http://dx.doi.org/10.1515/9783110583496-003.

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Brunner, Daniel, Julian Bueno, Xavier Porte, Sheler Maktoobi y Louis Andreoli. "4. Large scale spatiotemporal reservoirs". En Photonic Reservoir Computing, editado por Daniel Brunner, Miguel C. Soriano y Guy Van der Sande, 83–116. Berlin, Boston: De Gruyter, 2019. http://dx.doi.org/10.1515/9783110583496-004.

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Larger, Laurent. "6. Ikeda delay dynamics as Reservoir processors". En Photonic Reservoir Computing, editado por Daniel Brunner, Miguel C. Soriano y Guy Van der Sande, 153–84. Berlin, Boston: De Gruyter, 2019. http://dx.doi.org/10.1515/9783110583496-006.

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Van der Sande, Guy y Miguel C. Soriano. "7. Semiconductor lasers as reservoir substrates". En Photonic Reservoir Computing, editado por Daniel Brunner, Miguel C. Soriano y Guy Van der Sande, 185–204. Berlin, Boston: De Gruyter, 2019. http://dx.doi.org/10.1515/9783110583496-007.

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Actas de conferencias sobre el tema "Photonic computing"

Youngblood, Nathan, Paolo Pintus, Mario Dumont, Vivswan Shah, Toshiya Murai, Yuya Shoji, Duanni Huang y John Bowers. "Non-reciprocal devices for in-memory photonic computing". En Frontiers in Optics, FTu1D.2. Washington, D.C.: Optica Publishing Group, 2024. https://doi.org/10.1364/fio.2024.ftu1d.2.

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Non-reciprocal platforms can offer several key advantages for scalable and efficient photonic computing. In this talk, I will present our recent experimental work validating the use of non-reciprocal materials to implement high-endurance memory for photonic computing. Full-text article not available; see video presentation

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De Marinis, L., P. S. Kincaid, G. Contestabile, S. Gupta y N. Andriolli. "Photonic Technologies for Analog Neuromorphic Computing". En 2024 IEEE Photonics Society Summer Topicals Meeting Series (SUM), 1–2. IEEE, 2024. http://dx.doi.org/10.1109/sum60964.2024.10614512.

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Castro, Bernard J. Giron, Christophe Peucheret y Francesco Da Ros. "Microring Resonator-based Photonic Reservoir Computing". En 2024 24th International Conference on Transparent Optical Networks (ICTON), 1–4. IEEE, 2024. http://dx.doi.org/10.1109/icton62926.2024.10648245.

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Engheta, Nader. "Metamaterial Photonic Processing and Computing Machines". En 2024 IEEE INC-USNC-URSI Radio Science Meeting (Joint with AP-S Symposium), 127. IEEE, 2024. http://dx.doi.org/10.23919/inc-usnc-ursi61303.2024.10632286.

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Choi, Seou, Yannick Salamin, Charles Roques-Carmes, Rumen Dangovski, Di Luo, Zhuo Chen, Michael Horodynski, Jamison Sloan y Marin Soljačić. "Photonic Probabilistic Computing Leveraging Quantum Vacuum Noise". En CLEO: Science and Innovations, SF3J.5. Washington, D.C.: Optica Publishing Group, 2024. http://dx.doi.org/10.1364/cleo_si.2024.sf3j.5.

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We present a photonic probabilistic computing platform with a measurement-feedback scheme in a biased optical parametric oscillator. Probabilistic inference and generation of MNIST handwritten-digits are experimentally demonstrated.

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Ghasemi, Mehrdad, Hassan Kaatuzian, Houshyar Noshad y Mahdi NoroozOliaei. "Quantum Photonic Computer Challenges: Quantum Decoherence, Quantum Error Correction (QEC), and Scalability". En Frontiers in Optics, JD4A.42. Washington, D.C.: Optica Publishing Group, 2024. https://doi.org/10.1364/fio.2024.jd4a.42.

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The error correction process in quantum photonic computers (QPCs) and their loss in photons are two important parameters of considering them as reliable devices for computing very large and complicated problems concerning classical computers. In this paper, the new promising algorithms have been discussed for employment in quantum photonic computers as robust and reliable computational tasks to define the best fidelity as a figure of merit in quantum error correction schemes. Furthermore, a parametric study has been done by adding noble metals to the Integrated Photonic Chip.

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Gaur, Prabhav, Chengkuan Gao, Karl Johnson, Shimon Rubin, Yeshaiahu Fainman y Tzu-Chien Hsueh. "Optimization of hybrid photonic electrical reservoir computing". En Photonic Computing: From Materials and Devices to Systems and Applications, editado por Xingjie Ni y Wenshan Cai, 11. SPIE, 2024. http://dx.doi.org/10.1117/12.3027516.

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Kari, Sadra Rahimi, Allison Hastings, Nicholas A. Nobile, Dominique Pantin, Vivswan Shah y Nathan Youngblood. "Integrated Coherent Photonic Crossbar Arrays for Efficient Optical Computing". En CLEO: Science and Innovations, SM4M.6. Washington, D.C.: Optica Publishing Group, 2024. http://dx.doi.org/10.1364/cleo_si.2024.sm4m.6.

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We present a scalable approach to optical computing using coherent crossbar arrays for processing temporally multiplexed signals. Our design enables scalable matrix-matrix operations, and correlation detection, enabling efficient on-chip optical computing for diverse AI applications.

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Nakai, Makoto y Isamu Takai. "Nonlinear Silicon Photonic Passive Device for Edge Computing". En CLEO: Science and Innovations, STh1K.4. Washington, D.C.: Optica Publishing Group, 2024. http://dx.doi.org/10.1364/cleo_si.2024.sth1k.4.

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A silicon photonic passive device which performs nonlinear optical phase-to-amplitude signal conversion is proposed for edge computing applications. Classification of Iris and Wine datasets are demonstrated with accuracy of 100% and 97.75%, respectively.

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Uchida, Atsushi. "Artificial Intelligence Using Complex Photonics: Decision Making and Reservoir Computing". En Optical Fiber Communication Conference. Washington, D.C.: Optica Publishing Group, 2023. http://dx.doi.org/10.1364/ofc.2023.m2j.5.

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We overview recent development on photonic decision making and reservoir computing for artificial intelligence using complex photonics. Parallel implementations of photonic devices can accelerate information processing in decision making and reservoir computing.

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Informes sobre el tema "Photonic computing"

Hendry, Gilbert, Eric Robinson, Vitaliy Gleyzer, Johnnie Chan, Luca P. Carloni, Nadya Bliss y Keren Bergman. Circuit-Switched Memory Access in Photonic Interconnection Networks for High-Performance Embedded Computing. Fort Belvoir, VA: Defense Technical Information Center, julio de 2010. http://dx.doi.org/10.21236/ada532933.

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Hogle, Craig, Megan Ivory, Daniel Lobser, Brandon Ruzic y Christopher DeRose. Three-Photon Optical Pumping for Trapped Ion Quantum Computing. Office of Scientific and Technical Information (OSTI), septiembre de 2021. http://dx.doi.org/10.2172/1854752.

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Bossler, Kerry. Coupled Electron-Photon Monte Carlo Radiation Transport for Next-Generation Computing Systems. Office of Scientific and Technical Information (OSTI), septiembre de 2018. http://dx.doi.org/10.2172/1474024.

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Hemmer, Philip y Robert Armstrong. Fractal-Enhancement of Photon Band-Gap Cavities for Quantum Computing and Other Applications. Fort Belvoir, VA: Defense Technical Information Center, agosto de 2005. http://dx.doi.org/10.21236/ada444845.

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Guha, Supratik, H. S. Philip Wong, Jean Anne Incorvia y Srabanti Chowdhury. Future Directions Workshop: Materials, Processes, and R&D Challenges in Microelectronics. Defense Technical Information Center, junio de 2022. http://dx.doi.org/10.21236/ad1188476.

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Microelectronics is a complex field with ever-evolving technologies and business needs, fueled by decades of continued fundamental materials science and engineering advancement. Decades of dimensional scaling have led to the point where even the name microelectronics inadequately describes the field, as most modern devices operate on the nanometer scale. As we reach physical limits and seek more efficient ways for computing, research in new materials may offer alternative design approaches that involve much more than electron transport e.g. photonics, spintronics, topological materials, and a variety of exotic quasi-particles. New engineering processes and capabilities offer the means to take advantage of new materials designs e.g. 3D integration, atomic scale fabrication processes and metrologies, digital twins for semiconductor processes and microarchitectures. The wide range of potential technological approaches provides both opportunities and challenges. The Materials, Processes, and R and D Challenges in Microelectronics Future Directions workshop was held June 23-24, 2022, at the Basic Research Innovation Collaboration Center in Arlington, VA, to examine these opportunities and challenges. Sponsored by the Basic Research Directorate of the Office of the Under Secretary of Defense for Research and Engineering, it is intended as a resource for the S and T community including the broader federal funding community, federal laboratories, domestic industrial base, and academia.

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Quinn, Jarus W. Optical Computing. Organization of the 1993 Photonics Science Topical Meetings Held in Palm Springs, California on March 16 - 19, 1993. Technical Digest Series, Volume 7. Fort Belvoir, VA: Defense Technical Information Center, marzo de 1993. http://dx.doi.org/10.21236/ada269025.

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