Relevant bibliographies by topics / Neuromorphic applications

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

Academic literature on the topic 'Neuromorphic applications'

Author: Grafiati
Published: 6 September 2023
Last updated: 7 July 2024

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Journal articles on the topic "Neuromorphic applications"

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Mikki, Said. "Generalized Neuromorphism and Artificial Intelligence: Dynamics in Memory Space." Symmetry 16, no. 4 (April 18, 2024): 492. http://dx.doi.org/10.3390/sym16040492.

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This paper introduces a multidisciplinary conceptual perspective encompassing artificial intelligence (AI), artificial general intelligence (AGI), and cybernetics, framed within what we call the formalism of generalized neuromorphism. Drawing from recent advancements in computing, such as neuromorphic computing and spiking neural networks, as well as principles from the theory of open dynamical systems and stochastic classical and quantum dynamics, this formalism is tailored to model generic networks comprising abstract processing events. A pivotal aspect of our approach is the incorporation of the memory space and the intrinsic non-Markovian nature of the abstract generalized neuromorphic system. We envision future computations taking place within an expanded space (memory space) and leveraging memory states. Positioned at a high abstract level, generalized neuromorphism facilitates multidisciplinary applications across various approaches within the AI community.
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Park, Jisoo, Jihyun Shin, and Hocheon Yoo. "Heterostructure-Based Optoelectronic Neuromorphic Devices." Electronics 13, no. 6 (March 14, 2024): 1076. http://dx.doi.org/10.3390/electronics13061076.

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The concept of neuromorphic devices, aiming to process large amounts of information in parallel, at low power, high speed, and high efficiency, is to mimic the functions of human brain by emulating biological neural behavior. Optoelectronic neuromorphic devices are particularly suitable for neuromorphic applications with their ability to generate various pulses based on wavelength and to control synaptic stimulation. Each wavelength (ultraviolet, visible, and infrared) has specific advantages and optimal applications. Here, the heterostructure-based optoelectronic neuromorphic devices are explored across the full wavelength range (ultraviolet to infrared) by categorizing them on the basis of irradiated wavelength and structure (two-terminal and three-terminal) with respect to emerging optoelectrical materials. The relationship between neuromorphic applications, light wavelength, and mechanism is revisited. Finally, the potential and challenging aspects of next-generation optoelectronic neuromorphic devices are presented, which can assist in the design of suitable materials and structures for neuromorphic-based applications.
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Henkel, Jorg. "Stochastic Computing for Neuromorphic Applications." IEEE Design & Test 38, no. 6 (December 2021): 4. http://dx.doi.org/10.1109/mdat.2021.3126288.

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Diao, Yu, Yaoxuan Zhang, Yanran Li, and Jie Jiang. "Metal-Oxide Heterojunction: From Material Process to Neuromorphic Applications." Sensors 23, no. 24 (December 12, 2023): 9779. http://dx.doi.org/10.3390/s23249779.

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As technologies like the Internet, artificial intelligence, and big data evolve at a rapid pace, computer architecture is transitioning from compute-intensive to memory-intensive. However, traditional von Neumann architectures encounter bottlenecks in addressing modern computational challenges. The emulation of the behaviors of a synapse at the device level by ionic/electronic devices has shown promising potential in future neural-inspired and compact artificial intelligence systems. To address these issues, this review thoroughly investigates the recent progress in metal-oxide heterostructures for neuromorphic applications. These heterostructures not only offer low power consumption and high stability but also possess optimized electrical characteristics via interface engineering. The paper first outlines various synthesis methods for metal oxides and then summarizes the neuromorphic devices using these materials and their heterostructures. More importantly, we review the emerging multifunctional applications, including neuromorphic vision, touch, and pain systems. Finally, we summarize the future prospects of neuromorphic devices with metal-oxide heterostructures and list the current challenges while offering potential solutions. This review provides insights into the design and construction of metal-oxide devices and their applications for neuromorphic systems.
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Schuman, Catherine, Robert Patton, Shruti Kulkarni, Maryam Parsa, Christopher Stahl, N. Quentin Haas, J. Parker Mitchell, et al. "Evolutionary vs imitation learning for neuromorphic control at the edge*." Neuromorphic Computing and Engineering 2, no. 1 (January 24, 2022): 014002. http://dx.doi.org/10.1088/2634-4386/ac45e7.

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Abstract Neuromorphic computing offers the opportunity to implement extremely low power artificial intelligence at the edge. Control applications, such as autonomous vehicles and robotics, are also of great interest for neuromorphic systems at the edge. It is not clear, however, what the best neuromorphic training approaches are for control applications at the edge. In this work, we implement and compare the performance of evolutionary optimization and imitation learning approaches on an autonomous race car control task using an edge neuromorphic implementation. We show that the evolutionary approaches tend to achieve better performing smaller network sizes that are well-suited to edge deployment, but they also take significantly longer to train. We also describe a workflow to allow for future algorithmic comparisons for neuromorphic hardware on control applications at the edge.
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Kurshan, Eren, Hai Li, Mingoo Seok, and Yuan Xie. "A Case for 3D Integrated System Design for Neuromorphic Computing and AI Applications." International Journal of Semantic Computing 14, no. 04 (December 2020): 457–75. http://dx.doi.org/10.1142/s1793351x20500063.

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Over the last decade, artificial intelligence (AI) has found many applications areas in the society. As AI solutions have become more sophistication and the use cases grew, they highlighted the need to address performance and energy efficiency challenges faced during the implementation process. To address these challenges, there has been growing interest in neuromorphic chips. Neuromorphic computing relies on non von Neumann architectures as well as novel devices, circuits and manufacturing technologies to mimic the human brain. Among such technologies, three-dimensional (3D) integration is an important enabler for AI hardware and the continuation of the scaling laws. In this paper, we overview the unique opportunities 3D integration provides in neuromorphic chip design, discuss the emerging opportunities in next generation neuromorphic architectures and review the obstacles. Neuromorphic architectures, which relied on the brain for inspiration and emulation purposes, face grand challenges due to the limited understanding of the functionality and the architecture of the human brain. Yet, high-levels of investments are dedicated to develop neuromorphic chips. We argue that 3D integration not only provides strategic advantages to the cost-effective and flexible design of neuromorphic chips, it may provide design flexibility in incorporating advanced capabilities to further benefit the designs in the future.
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Huang, Heyi, Chen Ge, Zhuohui Liu, Hai Zhong, Erjia Guo, Meng He, Can Wang, Guozhen Yang, and Kuijuan Jin. "Electrolyte-gated transistors for neuromorphic applications." Journal of Semiconductors 42, no. 1 (January 1, 2021): 013103. http://dx.doi.org/10.1088/1674-4926/42/1/013103.

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Palmer, Chris. "Neuromorphic Computing Advances Deep-Learning Applications." Engineering 6, no. 8 (August 2020): 854–56. http://dx.doi.org/10.1016/j.eng.2020.06.010.

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Lv, Wenxing, Jialin Cai, Huayao Tu, Like Zhang, Rongxin Li, Zhe Yuan, Giovanni Finocchio, et al. "Stochastic artificial synapses based on nanoscale magnetic tunnel junction for neuromorphic applications." Applied Physics Letters 121, no. 23 (December 5, 2022): 232406. http://dx.doi.org/10.1063/5.0126392.

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Bio-inspired neuromorphic computing has aroused great interest due to its potential to realize on-chip learning with bio-plausibility and energy efficiency. Realizing spike-timing-dependent plasticity (STDP) in synaptic electronics is critical toward bio-inspired neuromorphic computing systems. Here, we report on stochastic artificial synapses based on nanoscale magnetic tunnel junctions that can implement STDP harnessing stochastic magnetization switching. We further demonstrate that both the magnitude and the temporal requirements for STDP can be modulated via engineering the pre- and post-synaptic voltage pulses. Moreover, based on arrays of binary magnetic synapses, unsupervised learning can be realized for neuromorphic computing tasks such as pattern recognition with great computing accuracy and efficiency. Our study suggests a potential route toward on-chip neuromorphic computing systems.
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Wang, Ye-Guo. "Applications of Memristors in Neural Networks and Neuromorphic Computing: A Review." International Journal of Machine Learning and Computing 11, no. 5 (September 2021): 350–56. http://dx.doi.org/10.18178/ijmlc.2021.11.5.1060.

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Dissertations / Theses on the topic "Neuromorphic applications"

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Chen, Xing. "Modeling and simulations of skyrmionic neuromorphic applications." Thesis, université Paris-Saclay, 2022. http://www.theses.fr/2022UPAST083.

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Les nanodispositifs spintroniques, qui exploitent à la fois les propriétés magnétiques et électriques des électrons, apportent diverses caractéristiques intéressantes et prometteuses pour le calcul neuromorphique. Les textures magnétiques, telles que les parois de domaine et les skyrmions, sont particulièrement intrigantes en tant que composants neuromorphiques, car elles peuvent prendre en charge différentes fonctionnalités grâce à la richesse de leurs mécanismes physiques. La façon dont la dynamique des skyrmions peut être utilisée pour construire du matériel neuromorphique économe en énergie, et comment l'apprentissage profond peut aider à réaliser des tests et des validations rapides et précis des propositions constituent les sujets centraux de cette thèse. Les principales contributions et innovations de cette thèse peuvent être résumées comme suit : 1. Études numériques et théoriques sur la dynamique des skyrmions dans les nanostructures confinées. Nous explorons la dynamique des skyrmions en termes de taille, de vitesse, d'énergie et de stabilité dans une nanopiste dont la largeur varie. Nous avons constaté que des skyrmions de petite taille pouvaient être obtenus en utilisant cette structure asymétrique. Nous obtenons également un compromis entre la largeur de la nanopiste (densité de stockage) et la vitesse de mouvement du skyrmion (vitesse d'accès aux données). Nous étudions la dynamique du skyrmion sous excitation de tension par l'effet d'anisotropie magnétique contrôlé par la tension dans un film mince circulaire. Nous constatons que le skyrmion respirant peut être analogisé comme un modulateur. Ces résultats pourraient nous aider à concevoir des dispositifs neuromorphiques efficaces. 2. Applications des dispositifs basés sur le skyrmion pour l'informatique neuromorphique. Nous présentons un dispositif compact de neurones de dopage Leaky-Integrate-Fire en exploitant la dynamique du skyrmion entraînée par le courant dans un nanotrack cunéiforme. Nous proposons un générateur de nombres aléatoires véritables basé sur le mouvement brownien thermique continu du skyrmion dans une géométrie confinée à température ambiante. Notre conception est prometteuse pour les systèmes de calcul neuromorphique émergents à faible puissance, tels que les réseaux neuronaux à impulsions et les réseaux neuronaux de calcul stochastique/probabiliste.3. Une approche axée sur les données pour la modélisation des systèmes physiques dynamiques basée sur les équations différentielles ordinaires (ODE) neuronales. Nous montrons que les formalismes adaptés des ODEs neurales, conçus pour la spintronique, peuvent prédire avec précision le comportement d'un nanodispositif non idéal, y compris le bruit, après entraînement sur un ensemble minimal de simulations micromagnétiques ou de données expérimentales, avec de nouvelles entrées et de nouveaux paramètres matériels n'appartenant pas aux données d'entraînement. Grâce à cette stratégie de modélisation, nous pouvons effectuer des tâches de calcul plus complexes, telles que les prédictions de séries temporelles Mackey-Glass et la reconnaissance de chiffres parlés, en utilisant les modèles entraînés de systèmes spintroniques, avec une précision élevée et une vitesse rapide par rapport aux simulations micromagnétiques conventionnelles
Spintronics nanodevices, which exploit both the magnetic and electrical properties of electrons, have emerged to bring various exciting characteristics promising for neuromorphic computing. Magnetic textures, such as domain walls and skyrmions, are particularly intriguing as neuromorphic components because they can support different functionalities due to their rich physical mechanisms. How the skyrmion dynamics can be utilized to build energy efficient neuromorphic hardware, and how deep learning can help achieve fast and accurate tests and validations of the proposals form the central topics of this thesis. The major contributions and innovations of this thesis can be summarized as follows: 1. Numerical and theoretical studies on skyrmion dynamics in confined nanostructures. We explore the skyrmion dynamics in terms of size, velocity, energy, and stability in a width-varying nanotrack. We found nanoscale skyrmion with small sizes could be obtained by employing this asymmetric structure. We also obtain a tradeoff between the nanotrack width (storage density) and the skyrmion motion velocity (data access speed). We study the skyrmion dynamics under voltage excitation through the voltage-controlled magnetic anisotropy effect in a circular thin film. We find that the breathing skyrmion can be analogized as a modulator. These findings could help us design efficient neuromorphic devices. 2. Skyrmion based device applications for neuromorphic computing. We present a compact Leaky-Integrate-Fire spiking neuron device by exploiting the current-driven skyrmion dynamics in a wedge-shaped nanotrack. We propose a True random number generators based on continuous skyrmion thermal Brownian motion in a confined geometry at room temperature. Our design are promising in emerging low power neuromorphic computing system, such as spiking neural network and stochastic/ probabilistic computing neuron network.3. A data-driven approach for modeling dynamical physical systems based on the Neural Ordinary Differential Equations (ODEs). We show that the adapted formalisms of Neural ODEs, designed for spintronics, can accurately predict the behavior of a non-ideal nanodevice, including noise, after training on a minimal set of micromagnetic simulations or experimental data, with new inputs and material parameters not belonging to the training data. With this modeling strategy, we can perform more complicated computational tasks, such as Mackey-Glass time-series predictions and spoken digit recognition, using the trained models of spintronic systems, with high accuracy and fast speed compared to conventional micromagnetic simulations
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Shi, Yuanyuan. "Two dimensional materials based electronic synapses for neuromorphic applications." Doctoral thesis, Universitat de Barcelona, 2018. http://hdl.handle.net/10803/663415.

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Electronic machines and computers have experienced a huge development during the last four decades, mainly thanks to the continuous scaling down of the hardware responsible of information processing and storage (i.e. transistors). However, as the size of these devices approaches inter-atomic distances, the fabrication costs increase exponentially. In order to solve this problem, the industry has started to consider new system architectures and hardware for processing and storing information. Inspired by nature, scientists and engineers have focused their attention on the human brain, which is the most powerful system known. The human brain can easily perform an infinity of operations that computers cannot do, it can naturally learn by adapting its physical structure, and it consumes much less energy. The reason is that human brains use a very sophisticated and dense neural network that process and stores the information in parallel. This massive parallelism is the genuine feature that even the most powerful computers developed to date cannot match, as they all rely in an architecture that process and stores information independently, creating a bottleneck that limits their performance. Therefore, emulating the functioning of the human brain using electronic circuits is extremely important, and it has become an obsession for the biggest enterprises. The first artificial neural networks for artificial intelligence (AI) systems relied on the use of field effect transistors, as they has been the basis of all modern electronic devices. However, recent studies indicate that memristors may be more suitable to emulate the interaction between neurons. More specifically, two neurons interact to each other through a synapse, which is a thin membrane that change its resistivity based on the electrical impulses released by the two neurons. The structure and working principle of synapses is strikingly similar to that of memristors, which moreover show the advantage of a simpler structure and a lower fabrication cost compared to transistors. However, not all memristors are suitable for emulating biological synapses. Most traditional memristors change their resistivity between two different states when a specific electrical impulse is applied. However, synapses change their resistivity with the time in a dynamic way, following some specific learning rules. In this PhD thesis I carry out a deep study about resistive switching in different materials, and I fabricate memristive devices that can accurately resemble several synaptic behaviors. One of the most innovative aspect of my investigation is that I use a new dielectric material (called hexagonal boron nitride) that holds a layered structure, and thanks to it my memristors show several properties never observed before. For example, Au/Ti/h-BN/Cu devices exhibit the coexistence of bipolar and threshold RS, which can be controlled by using different current limitations. The devices do not require forming process, due to the present of native defects in the h-BN stack during the growth. Doping the Cu substrate with Ni results in a lower amount of native defects, which reduces the current in high resistive state (but these devices require the use of a forming process). For both Cu and Ni-doped Cu electrodes, the current ON/OFF ratio can be improved by increasing the thickness of the h-BN stack. In Au/Ti/graphene/h-BN/graphene/Au devices the switching voltages increase and the currents in high resistive state are smaller than in the devices without graphene. The most probable reason for this observation is that multilayer graphene can block and slow down the migration of ions between the h-BN and the electrodes. Metal/h-BN/metal electronic synapses show an unprecedented relaxation process with very low variability in hundreds of cycles, and the power consumption is very low in both standby and volatile regime (i.e. 0.1 fW and 600 pW, respectively).
El cerebro humano puede realizar de forma sencilla infinidad de operaciones que los ordenadores no pueden hacer, pueden aprender naturalmente adaptando su estructura física, y consumen mucho menos energía. La razón es que el cerebro humano usa una sofisticada y muy densa red neuronal que procesa y almacena la información en paralelo. Este masivo paralelismo es la genuina característica que los ordenadores no pueden igualar, ya que éstos procesan y almacenan la información en unidades distintas, creando un embudo que limita sus prestaciones. Por lo tanto, emular el funcionamiento del cerebro utilizando componentes electrónicos es extremadamente importante, y se ha convertido en la obsesión de las mayores empresas. Las primeras redes neuronales artificiales para el desarrollo de inteligencia artificial están basadas en transistores, ya que éstos han sido la base de todos los dispositivos electrónicos modernos. Sin embargo, estudios recientes indican que los memristores podrían ser más idóneos para emular la interacción entre neuronas. En concreto, dos neuronas interactúan entre ellas a través de sinapsis, es decir, finas membranas que cambian su resistividad dependiendo de los impulsos eléctricos emitidos por las dos neuronas. La estructura y principio de funcionamiento de una sinapsis es muy similar al de un memristor, el cual presenta la ventaja de tener una estructura más simple y un coste de fabricación más bajo que un transistor. En esta tesis doctoral hemos desarrollado memristores avanzados utilizando materiales bidimensionales, como el grafeno y, especialmente, el nitruto de boto hexagonal con estructura multicapa. Nuestros experimentos y simulaciones indican que los dispositivos metal/h-BN/metal pueden ser utilizados como sinapsis electrónicas, ya que muestran comportamientos sinápticos en un único dispositivo. En nuestros dispositivos hemos observado short term plasticity, long term plasticity, spike timing dependent plasticity, y synapse relaxation. El régimen de funcionamiento puede ser controlado modificando la amplitud, duración e intervalo entre los pulsos aplicados. Además, las sinapsis electrónicas hechas mediante estructuras metal/h-BN/metal muestran un proceso de relajación muy repetitivo y con una baja variabilidad nunca observada anteriormente. Además, el consumo de potencia es muy bajo tanto en reposo (0.1 fW) como en modo volátil (600 pW).
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Uppala, Roshni. "Simulating Large Scale Memristor Based Crossbar for Neuromorphic Applications." University of Dayton / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1429296073.

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Lai, Qianxi. "Electrically configurable materials and devices for intelligent neuromorphic applications." Diss., Restricted to subscribing institutions, 2009. http://proquest.umi.com/pqdweb?did=1872061101&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.

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Mandal, Saptarshi. "Study of Mn doped HfO2 based Synaptic Devices for Neuromorphic Applications." University of Toledo / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1384535471.

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Pedró, Puig Marta. "Implementation of unsupervised learning mechanisms on OxRAM devices for neuromorphic computing applications." Doctoral thesis, Universitat Autònoma de Barcelona, 2019. http://hdl.handle.net/10803/667894.

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La present tesi recull els resultats de la recerca orientada a aportar una metodologia de caracterització elèctrica, modelat i simulació per a dispositius de commutació resistiva, quan es consideren aplicacions de computació neuromòrfica basades en aprenentatge no-supervisat, àmpliament demandades en l’actualitat com a solució de baix consum a les següents problemàtiques: per una banda, la limitació de la velocitat en la transferència de dades entre les unitats de memoria i processament que té lloc en les arquitectures de computador convencional (von Neumann). Per altra banda, la necessitat creixent de sistemes computacionals que realitzin tasques de classificació, anàlisi i inferència de quantitats massives de dades (per exemple, per a aplicacions de Big Data), junt amb tasques de detecció de patrons, predicció de comportaments i presa de decisions (aplicacions enfocades a Internet-of-Things, entre d’altres). En concret, s’investiguen els dispositius Oxide-based Resistive Random Access Memory (OxRAM) com a candidats per a la implementació electrònica de sinapsis en xarxes neuronals artificials físiques, altrament anomenades arquitectures neuromòrfiques. En primer lloc, es presenta una introducció teòrica a les diferents tecnologies electròniques amb propietats de commutació resistiva i memòria no volàtil, junt amb les figures de mèrit de cadascuna d’aquestes, tan demostrades com projectades segons l’International Roadmap for Devices and Systems de 2018. Amb aquest primer capítol, es pretén proveïr al lector de les bases necessàries per a poder comprendre els resultats exposats en els següents capítols. A continuació i mitjançant un enfocament bottom-up dividit en tres capítols, es tracten els procediments i resultats de la caracterització elèctrica i modelat dels dispositius estudiats per a la implementació de sinapsis electròniques analògiques. Com a punt de partida, es verifica experimentalment que els dispositius compleixen els requisits necessaris per a l’aplicació indicada. Al següent capítol, es demostren de forma experimental dues regles d’aprenentatge fonamentals per a poder executar algorismes d’aprenentatge autònoms (no supervisats) sobre una arquitectura neuromòrfica basada en els dispositius analitzats. Les regles d’aprenentatge demostrades permeten que els dispositius emulin procesos i mecanismes d’aprenentatge reportats en el camp de les neurociències, tals com la dependència temporal de la plasticitat, o el fenòmen de condicionament clàssic, per al qual es replica l’experiment dels gossos de Pavlov, permetent establir els fonaments de l’aprenentatge associatiu en dos o més dispositius. Per a concloure aquesta part relativa a les sinapsis electròniques analògiques, es proposa l’adaptació hardware d’un algorisme d’aprenentatge no supervisat. L’algorisme dissenyat permet que el sistema organitzi les seves connexions de forma autònoma i no supervisada, de tal manera que, un cop entrenada, la xarxa neuronal física mostri una organització topogràfica a la seva capa de sortida, que és característica de les regions del cervell biològic dedicades al processament de la informació sensorial. A més, el disseny del sistema permet concatenar diverses xarxes neuronals per a poder executar tasques cognitives de naturalesa més complexa, tals com l’associació de diferents atributs a un mateix concepte, permetent la computació jeràrquica. L’últim capítol està dedicat a l’estudi de dispositius OxRAM quan es considera un mode d’operació de baix consum, per a la implementació de sinapsis binàries. De nou, amb una perspectiva bottom-up, es parteix de la caracterització elèctrica i modelat dels dispositius, que en aquest cas constitueixen un xip neuromòrfic. Es verifica una regla d’aprenentatge probabilística, que després s’empra en un algorisme d’aprenentatge no supervisat dissenyat per a la inferència i predicció de seqüències periòdiques. Per acabar, es discuteixen les diferències i similituds entre els dos algorismes descrits a la tesi, i es proposa com es poden fer servir cadascun d’aquests de forma conjunta i complementària.
The present thesis compiles the results of the research oriented to provide a methodology for the electrical characterization, modeling and simulation of resistive switching devices, taking into consideration neuromorphic applications based on unsupervised learning This is widely demanded today as a low-consumption solution to the following issues: on the one hand, the speed limitations that take place in data transfer between the memory and processing units that takes place in conventional computer architectures. On the other hand, the growing need for low-power computational systems that perform tasks of classification, analysis and inference of massive amounts of data (for example, for Big Data applications), together with pattern recognition, prediction of behaviors and decision-making tasks (for applications focused on Internet-of-Things, among others). Specifically, Oxide-based Resistive Random Access Memory (OxRAM) devices are investigated as candidates for the electronic implementation of synapses in physical artificial neural networks, also referred to as neuromorphic architectures. First of all, a theoretical introduction to the different electronic technologies with resistive switching and non-volatile memory properties is provided. The figures of merit demonstrated and projected of each one of them are indicated according to the International Roadmap for Devices and Systems of 2018. With this first chapter, the intention is to provide the reader with the necessary background required to understand the results outlined in the following chapters. Next, and by using a bottom-up approach divided into the three following chapters, the procedures and results of the electrical characterization and modeling of the OxRAM devices studied for the implementation of analog electronic synapses are discussed. As a starting point, it is experimentally verified that the devices meet the requirements for the indicated application. In the following chapter, two fundamental learning rules are demonstrated experimentally in order to permit the execution of an autonomous (unsupervised) learning algorithm on a neuromorphic architecture based on the tested devices. The proven learning rules allow the devices to emulate certain processes and learning mechanisms reported in the neuroscience field, such as spike-timing dependent plasticity, or the classical conditioning phenomenon, for which Pavlov’s dog experiment is replicated as to establish the foundations of associative learning, to be implemented between two or more synaptic devices. To conclude this part related to analog electronic synapses, the hardware adaptation of an unsupervised learning algorithm is proposed. The designed algorithm provides the system with the property of self-organization, in such a way that, once trained, the physical neuronal network shows a topographical organization in its output layer, which is characteristic of the sensory processing areas of the biological brain. Furthermore, the proposed design and algorithm allow the concatenation of several neuronal networks, in order to execute cognitive tasks of a more complex nature, such as the association of different attributes to the same concept, related to hierarchical computation. The last chapter is dedicated to the study of OxRAM devices when a low-power mode is considered, for the implementation of binary synapses. Again using a bottom-up perspective, the chapter begins with the electrical characterization and modeling of the devices, which in this case constitute a neuromorphic chip. A probabilistic learning rule is demonstrated, which is then used in an unsupervised on-line learning algorithm designed for the inference and prediction of periodic temporal sequences. Finally, the differences and similarities between the two algorithms described in the thesis are discussed, and a proposal is made as to how each of these can be used in a joint and complementary way.
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Petre, Csaba. "Sim2spice a tool for compiling simulink designs on FPAA and applications to neuromorphic circuits /." Thesis, Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31820.

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Thesis (M. S.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2010.
Committee Chair: Paul Hasler; Committee Member: Christopher Rozell; Committee Member: David Anderson. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Herrmann, Eric. "A Novel Gate Controlled Metal Oxide Resistive Memory Cell and its Applications." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1540565326482153.

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MARRONE, FRANCESCO. "Memristor-based hardware accelerators: from device modeling to AI applications." Doctoral thesis, Politecnico di Torino, 2022. http://hdl.handle.net/11583/2972305.

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SECCO, JACOPO. "Memristor Platforms for Pattern Recognition Memristor Theory, Systems and Applications." Doctoral thesis, Politecnico di Torino, 2017. http://hdl.handle.net/11583/2680573.

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In the last decade a large scientific community has focused on the study of the memristor. The memristor is thought to be by many the best alternative to CMOS technology, which is gradually showing its flaws. Transistor technology has developed fast both under a research and an industrial point of view, reducing the size of its elements to the nano-scale. It has been possible to generate more and more complex machinery and to communicate with that same machinery thanks to the development of programming languages based on combinations of boolean operands. Alas as shown by Moore’s law, the steep curve of implementation and of development of CMOS is gradually reaching a plateau. It is clear the need of studying new elements that can combine the efficiency of transistors and at the same time increase the complexity of the operations. Memristors can be described as non-linear resistors capable of maintaining memory of the resistance state that they reached. From their first theoretical treatment by Professor Leon O. Chua in 1971, different research groups have devoted their expertise in studying the both the fabrication and the implementation of this new promising technology. In the following thesis a complete study on memristors and memristive elements is presented. The road map that characterizes this study departs from a deep understanding of the physics that govern memristors, focusing on the HP model by Dr. Stanley Williams. Other devices such as phase change memories (PCMs) and memristive biosensors made with Si nano-wires have been studied, developing emulators and equivalent circuitry, in order to describe their complex dynamics. This part sets the first milestone of a pathway that passes trough more complex implementations such as neuromorphic systems and neural networks based on memristors proving their computing efficiency. Finally it will be presented a memristror-based technology, covered by patent, demonstrating its efficacy for clinical applications. The presented system has been designed for detecting and assessing automatically chronic wounds, a syndrome that affects roughly 2% of the world population, through a Cellular Automaton which analyzes and processes digital images of ulcers. Thanks to its precision in measuring the lesions the proposed solution promises not only to increase healing rates, but also to prevent the worsening of the wounds that usually lead to amputation and death.
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Books on the topic "Neuromorphic applications"

1

Kozma, Robert, Robinson E. Pino, and Giovanni E. Pazienza, eds. Advances in Neuromorphic Memristor Science and Applications. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-4491-2.

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Kozma, Robert. Advances in Neuromorphic Memristor Science and Applications. Dordrecht: Springer Netherlands, 2012.

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Beaton, Paul Timothy, ed. Frontiers in Memristive Materials for Neuromorphic Processing Applications. Washington, D.C.: National Academies Press, 2020. http://dx.doi.org/10.17226/25938.

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C, Merrill Walter, and United States. National Aeronautics and Space Administration. Scientific and Technical Information Division., eds. Neuromorphic learning of continuous-valued mappings from noise-corrupted data: Application to real-time adaptive control. [Washington, DC]: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Division, 1990.

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Bartolozzi, Chiara, Emre O. Neftci, and Elisabetta Chicca, eds. Neuromorphic Engineering Systems and Applications. Frontiers Media SA, 2021. http://dx.doi.org/10.3389/978-2-88971-723-1.

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van Schaik, André, Tobi Delbruck, and Jennifer Hasler, eds. Neuromorphic Engineering Systems and Applications. Frontiers Media SA, 2015. http://dx.doi.org/10.3389/978-2-88919-454-4.

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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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Pazienza, Giovanni E., Robert Kozma, and Robinson E. Pino. Advances in Neuromorphic Memristor Science and Applications. Springer Netherlands, 2016.

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Advances In Neuromorphic Memristor Science And Applications. Springer, 2012.

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Book chapters on the topic "Neuromorphic applications"

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Narduzzi, Simon, Loreto Mateu, Petar Jokic, Erfan Azarkhish, and Andrea Dunbar. "Benchmarking Neuromorphic Computing for Inference." In Industrial Artificial Intelligence Technologies and Applications, 1–19. New York: River Publishers, 2023. http://dx.doi.org/10.1201/9781003377382-1.

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Milo, Valerio, Gerardo Malavena, Christian Monzio Compagnoni, and Daniele Ielmini. "Memristive/CMOS Devices for Neuromorphic Applications." In Springer Handbook of Semiconductor Devices, 1167–99. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-79827-7_32.

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Lu, Wei. "RRAM Fabric for Neuromorphic Computing Applications." In From Artificial Intelligence to Brain Intelligence, 175–90. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003338215-10.

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Gómez-Vilda, Pedro, José Manuel Ferrández-Vicente, Victoria Rodellar-Biarge, Agustín Álvarez-Marquina, Luis Miguel Mazaira-Fernández, Rafael Martínez-Olalla, and Cristina Muñoz-Mulas. "Neuromorphic Detection of Vowel Representation Spaces." In New Challenges on Bioinspired Applications, 1–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21326-7_1.

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Hu, Xiaofang, Shukai Duan, Wenbo Song, Jiagui Wu, and Pinaki Mazumder. "Memristor-based Cellular Nonlinear/Neural Network: Design, Analysis and Applications." In Neuromorphic Circuits for Nanoscale Devices, 275–301. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003338918-11.

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Pino, Robinson E. "Computational Intelligence and Neuromorphic Computing Architectures." In Advances in Neuromorphic Memristor Science and Applications, 77–88. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-4491-2_6.

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Isik, Murat, Hiruna Vishwamith, Yusuf Sur, Kayode Inadagbo, and I. Can Dikmen. "NEUROSEC: FPGA-Based Neuromorphic Audio Security." In Applied Reconfigurable Computing. Architectures, Tools, and Applications, 134–47. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-55673-9_10.

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Ryan, Kevin, Sansiri Tanachutiwat, and Wei Wang. "3D CMOL Crossnet for Neuromorphic Network Applications." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 1–5. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02427-6_1.

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Dias, C., J. Ventura, and P. Aguiar. "Memristive-Based Neuromorphic Applications and Associative Memories." In Advances in Memristors, Memristive Devices and Systems, 305–42. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51724-7_13.

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Maji, Prasenjit, Ramapati Patra, Kunal Dhibar, and Hemanta Kumar Mondal. "SNN Based Neuromorphic Computing Towards Healthcare Applications." In Internet of Things. Advances in Information and Communication Technology, 261–71. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-45878-1_18.

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Conference papers on the topic "Neuromorphic applications"

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Shastri, Bhavin J., Alexander N. Tait, Mitchell A. Nahmias, Thomas Ferreira de Lima, Hsuan-Tung Peng, and Paul R. Prucnal. "Neuromorphic Photonic Processor Applications." In 2019 IEEE Photonics Society Summer Topical Meeting Series (SUM). IEEE, 2019. http://dx.doi.org/10.1109/phosst.2019.8795013.

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Prucnal, Paul R., Alexander N. Tait, Mitchell A. Nahmias, Thomas Ferreira de Lima, Hsuan-Tung Peng, and Bhavin J. Shastri. "Multiwavelength Neuromorphic Photonics." In CLEO: Applications and Technology. Washington, D.C.: OSA, 2019. http://dx.doi.org/10.1364/cleo_at.2019.jm3m.3.

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Brückerhoff-Plückelmann, Frank, Johannes Feldmann, Helge Gehring, Wen Zhou, C. David Wright, Harish Bhaskaran, and Wolfram Pernice. "Ultra-low Crosstalk Multiplexer for Neuromorphic Photonic Data Processing." In CLEO: Applications and Technology. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/cleo_at.2022.jth3a.51.

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Neuromorphic architectures aim for powering artificial intelligence algorithms energy efficiently. We fabricate a neuromorphic photonic circuit for matrix vector multiplications using phase change materials and integrate wavelength multiplexers with a crosstalk below -41 dB on-chip.
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Aimone, James B., Ojas Parekh, and William Severa. "Neural computing for scientific computing applications." In NCS '17: Neuromorphic Computing Symposium. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3183584.3183618.

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Patton, Robert, Prasanna Date, Shruti Kulkarni, Chathika Gunaratne, Seung-Hwan Lim, Guojing Cong, Steven R. Young, Mark Coletti, Thomas E. Potok, and Catherine D. Schuman. "Neuromorphic Computing for Scientific Applications." In 2022 IEEE/ACM Redefining Scalability for Diversely Heterogeneous Architectures Workshop (RSDHA). IEEE, 2022. http://dx.doi.org/10.1109/rsdha56811.2022.00008.

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Cardwell, Suma G., and Frances S. Chance. "Dendritic Computation for Neuromorphic Applications." In ICONS '23: 2023 International Conference on Neuromorphic Systems. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3589737.3606001.

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Buckley, S. M., J. Chiles, A. N. McCaughan, R. P. Mirin, S. W. Nam, and J. M. Shainline. "Light sources for neuromorphic computing." In CLEO: Applications and Technology. Washington, D.C.: OSA, 2018. http://dx.doi.org/10.1364/cleo_at.2018.jw2a.29.

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Prucnal, Paul R., and Thomas Ferreira de Lima. "Neuromorphic photonics for real-time applications." In Emerging Topics in Artificial Intelligence 2020, edited by Giovanni Volpe, Joana B. Pereira, Daniel Brunner, and Aydogan Ozcan. SPIE, 2020. http://dx.doi.org/10.1117/12.2571477.

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Etienne-Cummings, Ralph, Swati Mehta, Ralf Philipp, and Viktor Gruev. "Neuromorphic Vision Systems for Mobile Applications." In IEEE Custom Integrated Circuits Conference 2006. IEEE, 2006. http://dx.doi.org/10.1109/cicc.2006.320906.

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Forsell, Jr., Robert, Allison L. Thornbrugh, and Carl A. Preyer. "Applications of smart neuromorphic focal planes." In San Dieg - DL Tentative, edited by John C. Carson. SPIE, 1990. http://dx.doi.org/10.1117/12.23006.

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Reports on the topic "Neuromorphic applications"

1

Davis, Joel L. Neuromorphic Systems: From Biological Foundations to System Properties and Real World Applications. Fort Belvoir, VA: Defense Technical Information Center, December 1997. http://dx.doi.org/10.21236/ada333498.

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Potok, Thomas, Catherine Schuman, Robert Patton, Todd Hylton, Hai Li, and Robinson Pino. Neuromorphic Computing, Architectures, Models, and Applications. A Beyond-CMOS Approach to Future Computing, June 29-July 1, 2016, Oak Ridge, TN. Office of Scientific and Technical Information (OSTI), December 2016. http://dx.doi.org/10.2172/1341738.

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