Relevant bibliographies by topics / Neuromorphic photonics

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Neuromorphic photonics'

Author: Grafiati
Published: 5 June 2025
Last updated: 24 June 2025

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Journal articles on the topic "Neuromorphic photonics"

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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 (2017): 577–99. http://dx.doi.org/10.1515/nanoph-2016-0139.

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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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Kutluyarov, Ruslan V., Aida G. Zakoyan, Grigory S. Voronkov, Elizaveta P. Grakhova, and Muhammad A. Butt. "Neuromorphic Photonics Circuits: Contemporary Review." Nanomaterials 13, no. 24 (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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Xu, Bo, Yuhao Huang, Yuetong Fang, Zhongrui Wang, Shaoliang Yu, and Renjing Xu. "Recent Progress of Neuromorphic Computing Based on Silicon Photonics: Electronic–Photonic Co-Design, Device, and Architecture." Photonics 9, no. 10 (2022): 698. http://dx.doi.org/10.3390/photonics9100698.

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The rapid development of neural networks has led to tremendous applications in image segmentation, speech recognition, and medical image diagnosis, etc. Among various hardware implementations of neural networks, silicon photonics is considered one of the most promising approaches due to its CMOS compatibility, accessible integration platforms, mature fabrication techniques, and abundant optical components. In addition, neuromorphic computing based on silicon photonics can provide massively parallel processing and high-speed operations with low power consumption, thus enabling further exploration of neural networks. Here, we focused on the development of neuromorphic computing based on silicon photonics, introducing this field from the perspective of electronic–photonic co-design and presenting the architecture and algorithm theory. Finally, we discussed the prospects and challenges of neuromorphic silicon photonics.
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Ferreira de Lima, Thomas, Alexander N. Tait, Armin Mehrabian, et al. "Primer on silicon neuromorphic photonic processors: architecture and compiler." Nanophotonics 9, no. 13 (2020): 4055–73. http://dx.doi.org/10.1515/nanoph-2020-0172.

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AbstractMicroelectronic computers have encountered challenges in meeting all of today’s demands for information processing. Meeting these demands will require the development of unconventional computers employing alternative processing models and new device physics. Neural network models have come to dominate modern machine learning algorithms, and specialized electronic hardware has been developed to implement them more efficiently. A silicon photonic integration industry promises to bring manufacturing ecosystems normally reserved for microelectronics to photonics. Photonic devices have already found simple analog signal processing niches where electronics cannot provide sufficient bandwidth and reconfigurability. In order to solve more complex information processing problems, they will have to adopt a processing model that generalizes and scales. Neuromorphic photonics aims to map physical models of optoelectronic systems to abstract models of neural networks. It represents a new opportunity for machine information processing on sub-nanosecond timescales, with application to mathematical programming, intelligent radio frequency signal processing, and real-time control. The strategy of neuromorphic engineering is to externalize the risk of developing computational theory alongside hardware. The strategy of remaining compatible with silicon photonics externalizes the risk of platform development. In this perspective article, we provide a rationale for a neuromorphic photonics processor, envisioning its architecture and a compiler. We also discuss how it can be interfaced with a general purpose computer, i.e. a CPU, as a coprocessor to target specific applications. This paper is intended for a wide audience and provides a roadmap for expanding research in the direction of transforming neuromorphic photonics into a viable and useful candidate for accelerating neuromorphic computing.
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Nahmias, Mitchell A., Bhavin J. Shastri, Alexander N. Tait, Thomas Ferreira de Lima, and Paul R. Prucnal. "Neuromorphic Photonics." Optics and Photonics News 29, no. 1 (2018): 34. http://dx.doi.org/10.1364/opn.29.1.000034.

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Li, Tiantian, Yijie Li, Yuteng Wang, et al. "Neuromorphic Photonics Based on Phase Change Materials." Nanomaterials 13, no. 11 (2023): 1756. http://dx.doi.org/10.3390/nano13111756.

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Neuromorphic photonics devices based on phase change materials (PCMs) and silicon photonics technology have emerged as promising solutions for addressing the limitations of traditional spiking neural networks in terms of scalability, response delay, and energy consumption. In this review, we provide a comprehensive analysis of various PCMs used in neuromorphic devices, comparing their optical properties and discussing their applications. We explore materials such as GST (Ge2Sb2Te5), GeTe-Sb2Te3, GSST (Ge2Sb2Se4Te1), Sb2S3/Sb2Se3, Sc0.2Sb2Te3 (SST), and In2Se3, highlighting their advantages and challenges in terms of erasure power consumption, response rate, material lifetime, and on-chip insertion loss. By investigating the integration of different PCMs with silicon-based optoelectronics, this review aims to identify potential breakthroughs in computational performance and scalability of photonic spiking neural networks. Further research and development are essential to optimize these materials and overcome their limitations, paving the way for more efficient and high-performance photonic neuromorphic devices in artificial intelligence and high-performance computing applications.
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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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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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de Lima, Thomas Ferreira, Hsuan-Tung Peng, Alexander N. Tait, et al. "Machine Learning With Neuromorphic Photonics." Journal of Lightwave Technology 37, no. 5 (2019): 1515–34. http://dx.doi.org/10.1109/jlt.2019.2903474.

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Pitruzzello, Giampaolo. "Neuromorphic photonics for efficient computing." Nature Photonics 19, no. 4 (2025): 350–51. https://doi.org/10.1038/s41566-025-01654-9.

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Argyris, Apostolos. "Photonic neuromorphic technologies in optical communications." Nanophotonics 11, no. 5 (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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Dissertations / Theses on the topic "Neuromorphic photonics"

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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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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"<br>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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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<br>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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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<br>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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Masominia, Amir Hossein. "Neuro-inspired computing with excitable microlasers." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASP053.

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Cette thèse présente des recherches sur des systèmes de calcul alternatifs, en se concentrant spécifiquement sur le calcul analogique et neuromimétique. La quête d'une intelligence artificielle plus générale a mis en évidence les limitations des unités de calcul conventionnelles basées sur les architectures de Von Neumann, en particulier en termes d'efficacité énergétique et de complexité. Les architectures de calcul inspirées du cerveau et les ordinateurs analogiques sont des prétendants de premier plan dans ce domaine. Parmi les différentes possibilités, les systèmes photoniques impulsionnels (spiking) offrent des avantages significatifs en termes de vitesse de traitement, ainsi qu'une efficacité énergétique accrue. Nous proposons une approche novatrice pour les tâches de classification et de reconnaissance d'images en utilisant un laser à micropilier développé en interne fonctionnant comme un neurone artificiel. La non-linéarité du laser excitable, résultant des dynamiques internes, permet de projeter les informations entrantes, injectées optiquement dans le micropilier au travers de son gain, dans des dimensions supérieures. Cela permet de trouver des régions linéairement séparables pour la classification. Le micropilier laser excitable présente toutes les propriétés fondamentales d'un neurone biologique, y compris l'excitabilité, la période réfractaire et l'effet de sommation, avec des échelles caractéristiques de fonctionnement sous la nanoseconde. Cela en fait un candidat de premier choix dans les systèmes impulsionnels où la dynamique de l'impulsion elle-même porte des informations, par opposition aux systèmes qui considèrent uniquement la fréquence moyenne des impulsions. Nous avons conçu et étudié plusieurs systèmes utilisant le micropilier laser, basés sur un calculateur à réservoir à nœud physique unique qui émule un calculateur à plusieurs noeuds et utilisant différents régimes dynamiques du microlaser. Ces systèmes ont atteint des performances de reconnaissance plus élevées par rapport aux systèmes sans le microlaser. De plus, nous introduisons un nouveau modèle inspiré des champs réceptifs dans le cortex visuel, capable de classifier un ensemble de chiffres tout en éliminant le besoin d'un ordinateur conventionnel dans le processus. Ce système a été mis en œuvre expérimentalement avec succès en utilisant une configuration optique combinée en espace libre et fibrée, ouvrant des perspectives intéressantes pour le calcul analogue ultra-rapide sur architecture matérielle<br>This thesis presents research on alternative computing systems, with a focus on analog and neuromimetic computing. The pursuit of more general artificial intelligence has underscored limitations in conventional computing units based on Von Neumann architectures, particularly regarding energy efficiency and complexity. Brain-inspired computing architectures and analog computers are key contenders in this field. Among the various proposed methods, photonic spiking systems offer significant advantages in processing and communication speeds, as well as potential energy efficiency. We propose a novel approach to classification and image recognition tasks using an in-house developed micropillar laser as the artificial neuron. The nonlinearity of the spiking micropillar laser, resulting from the internal dynamics of the system, allows for mapping incoming information, optically injected to the micropillar through gain, into higher dimensions. This enables finding linearly separable regions for classification. The micropillar laser exhibits all fundamental properties of a biological neuron, including excitability, refractory period, and summation effect, with sub-nanosecond characteristic timescales. This makes it a strong candidate in spiking systems where the dynamics of the spike itself carries information, as opposed to systems that consider spiking rates only. We designed and studied several systems using the micropillar laser, based on a reservoir computer with a single physical node that emulates a reservoir computer with several nodes, using different dynamical regimes of the microlaser. These systems achieved higher performance in prediction accuracy of the classes compared to systems without the micropillar. Additionally, we introduce a novel system inspired by receptive fields in the visual cortex, capable of classifying a digit dataset entirely online, eliminating the need for a conventional computer in the process. This system was successfully implemented experimentally using a combined fiber and free-space optical setup, opening promising prospects for ultra-fast, hardware based feature selection and classification systems
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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<br>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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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<br>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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Books on the topic "Neuromorphic photonics"

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Prucnal, Paul R., and Bhavin J. Shastri. Neuromorphic Photonics. Taylor & Francis Group, 2017.

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Prucnal, Paul R., Bhavin J. Shastri, and Malvin Carl Teich. Neuromorphic Photonics. Edited by Paul R. Prucnal and Bhavin J. Shastri. CRC Press, 2017. http://dx.doi.org/10.1201/9781315370590.

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Prucnal, Paul R., and Bhavin J. Shastri. Neuromorphic Photonics. Taylor & Francis Group, 2017.

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Prucnal, Paul R., and Bhavin J. Shastri. Neuromorphic Photonics. Taylor & Francis Group, 2017.

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Prucnal, Paul R., and Bhavin J. Shastri. Neuromorphic Photonics. Taylor & Francis Group, 2017.

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Prucnal, Paul R., and Bhavin J. Shastri. Neuromorphic Photonics. Taylor & Francis Group, 2017.

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Photonic Neuromorphic Computing. Taylor & Francis Group, 2017.

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Dong, Yibo, Min Gu, Elena Goi, Yangyundou Wang, and Zhengfen Wan. Neuromorphic Photonic Devices and Applications. Elsevier, 2023.

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Wang, Jing, Min Gu, Elena Goi, Yangyundou Wang, and Zhengfen Wan. Neuromorphic Photonic Devices and Applications. Elsevier, 2023.

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Book chapters on the topic "Neuromorphic photonics"

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Shastri, Bhavin J., Alexander N. Tait, Thomas Ferreira de Lima, Mitchell A. Nahmias, Hsuan-Tung Peng, and Paul R. Prucnal. "Neuromorphic Photonics, Principles of." In Encyclopedia of Complexity and Systems Science. Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-642-27737-5_702-1.

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Shastri, Bhavin J., Alexander N. Tait, Thomas Ferreira de Lima, Mitchell A. Nahmias, Hsuan-Tung Peng, and Paul R. Prucnal. "Principles of Neuromorphic Photonics." In Unconventional Computing. Springer US, 2018. http://dx.doi.org/10.1007/978-1-4939-6883-1_702.

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Choure, Kamal Kishor, Ankur Saharia, Nitesh Mudgal, Manisha Prajapat, Manish Tiwari, and Ghanshyam Singh. "An introduction to neuromorphic computing." In Intelligent Photonics Systems. CRC Press, 2025. https://doi.org/10.1201/9781003591030-7.

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Marquez, Bicky A., Chaoran Huang, Paul R. Prucnal, and Bhavin J. Shastri. "Neuromorphic Silicon Photonics for Artificial Intelligence." In Topics in Applied Physics. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68222-4_10.

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Pappas, Christos, Andrea Demarchi, Ioannis Roumpos, et al. "An Ultra-Small InP Microdisk Laser Diode for Programmable Non-linear Activation Functions in Neuromorphic Photonics." In The 25th European Conference on Integrated Optics. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-63378-2_66.

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Tait, Alexander N., Mitchell A. Nahmias, Yue Tian, Bhavin J. Shastri, and Paul R. Prucnal. "Photonic Neuromorphic Signal Processing and Computing." In Nanophotonic Information Physics. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40224-1_8.

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Haidar Shahine, Mike. "Neuromorphic Photonics." In Optoelectronics [Working Title]. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.94297.

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Neuromorphic photonic applies concepts extracted from neuroscience to develop photonic devices behaving like neural systems and achieve brain-like information processing capacity and efficiency. This new field combines the advantages of photonics and neuromorphic architectures to build systems with high efficiency, high interconnectivity and paves the way to ultrafast, power efficient and low cost and complex signal processing. We explore the use of semiconductor lasers with optoelectronic feedback operating in self-pulsating mode as photonic neuron that can deliver flexible control schemes with narrow optical pulses of less than 30 ps pulse width, with adjustable pulse intervals of −2 ps/mA to accommodate specific Pulse Position Modulation (PPM) coding of events to trigger photonic neuron firing as required. The analyses cover in addition to self-pulsation performance and controls, the phase noise and jitter characteristics of such solution.
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Prucnal, Paul R., Bhavin J. Shastri, and Malvin Carl Teich. "Silicon Photonics." In Neuromorphic Photonics. CRC Press, 2017. http://dx.doi.org/10.1201/9781315370590-7.

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Prucnal, Paul R., Bhavin J. Shastri, and Malvin Carl Teich. "Neuromorphic Engineering." In Neuromorphic Photonics. CRC Press, 2017. http://dx.doi.org/10.1201/9781315370590-1.

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Prucnal, Paul R., Bhavin J. Shastri, and Malvin Carl Teich. "Primer on Photonics." In Neuromorphic Photonics. CRC Press, 2017. http://dx.doi.org/10.1201/9781315370590-3.

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Conference papers on the topic "Neuromorphic photonics"

1

Flodgren, Vidar, Abhijit Das, Joachim E. Sestoft, et al. "On-Chip Light Transmission between Nanoscale Optoelectronic Devices." In British and Irish Conference on Optics and Photonics. Optica Publishing Group, 2024. https://doi.org/10.1364/bicop.2024.f4a.4.

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On-chip light broadcasting can reduce spatial footprint and enhance energy efficiency in photonic neuromorphic systems. We demonstrate on-chip light transmission between InP nanowire photodiodes on silicon, addressing the gap towards complete nanoscale photonic integrated circuits.
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De Marinis, L., P. S. Kincaid, G. Contestabile, S. Gupta, and N. Andriolli. "Photonic Technologies for Analog Neuromorphic Computing." In 2024 IEEE Photonics Society Summer Topicals Meeting Series (SUM). IEEE, 2024. http://dx.doi.org/10.1109/sum60964.2024.10614512.

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Sedov, E. S., and A. V. Kavokin. "Neuromorphic Networks with Exciton Polariton Lattices." In 2024 Photonics & Electromagnetics Research Symposium (PIERS). IEEE, 2024. http://dx.doi.org/10.1109/piers62282.2024.10618484.

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Kruger, Nimrod, Sami El Arja, Evie Andrew, Travis Monk, and Andre van Schaik. "Performance metrics for neuromorphic imaging." In Quantum Sensing and Nano Electronics and Photonics XXI, edited by Manijeh Razeghi, Giti A. Khodaparast, and Miriam S. Vitiello. SPIE, 2025. https://doi.org/10.1117/12.3041873.

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Zakoyan, Aida G., Grigory S. Voronkov, Ekaterina A. Lopukhova, Ivan V. Stepanov, Elizaveta P. Grakhova, and Ruslan V. Kutluyarov. "Neuromorphic photonics circuit for efficient multichannel signal coding." In Optical Technologies for Telecommunications 2023, edited by Oleg G. Morozov, Albert C. Sultanov, and Anton V. Bourdine. SPIE, 2024. http://dx.doi.org/10.1117/12.3026590.

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Diamantopoulos, Nikolaos-Panteleimon, Takuro Fujii, Suguru Yamaoka, Hidetaka Nishi, Koji Takeda, and Shinji Matsuo. "Ultrafast Membrane Lasers with Optical Feedback for Optical Interconnects and Neuromorphic Computing." In Integrated Photonics Research, Silicon and Nanophotonics. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/iprsn.2024.itu2b.1.

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We present the first 16-channel membrane laser array exhibiting photon-photon resonance, achieving 50-GHz bandwidths and &lt;130 fJ/bit energies, for 1.6 Tbps transceivers. Additionally, we have showcased ultra-fast and energy-efficient spiking dynamics for neuromorphic applications.
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Misilin, V. A., V. A. Kiselev, A. A. Mikhailov, R. V. Petrov, and A. O. Nikitin. "Magnetoelectric Basic Logic Element for Neuromorphic Computing." In 2024 Photonics & Electromagnetics Research Symposium (PIERS). IEEE, 2024. http://dx.doi.org/10.1109/piers62282.2024.10618426.

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Salpietro, Salvatore, Marco Novarese, and Mariangela Gioannini. "Study of microring nonlinearities in silicon photonics for neuromorphic computing." In Silicon Photonics XX, edited by Graham T. Reed and Jonathan Bradley. SPIE, 2025. https://doi.org/10.1117/12.3040062.

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Kincaid, P. S., N. Andriolli, G. Contestabile, and L. De Marinis. "Resolution Limits in Optical Microring Modulators for Applications in Neuromorphic Photonics." In 2024 IEEE Photonics Conference (IPC). IEEE, 2024. https://doi.org/10.1109/ipc60965.2024.10799676.

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Gupta, Neha, Vikas Maheshwari, and Sukhdev Roy. "Optogenetics switching control and application for neuromorphic computing." In 2nd International Conference on Current Trends in Physics and Photonics (ICCTPP 2024), edited by Debabrata Saha and Aavishkar Katti. SPIE, 2024. http://dx.doi.org/10.1117/12.3041716.

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