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Relevant bibliographies by topics / Neuromorphic technologies

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Academic literature on the topic 'Neuromorphic technologies'

Author: Grafiati

Published: 15 June 2024

Last updated: 31 July 2025

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Journal articles on the topic "Neuromorphic technologies"

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Okazaki, Atsuya. "Hardware Technologies for Neuromorphic Computing." Journal of the Robotics Society of Japan 35, no. 3 (2017): 209–14. http://dx.doi.org/10.7210/jrsj.35.209.

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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. Lat

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Vianello, Elisa, and Melika Payvand. "Scaling neuromorphic systems with 3D technologies." Nature Electronics 7, no. 6 (2024): 419–21. http://dx.doi.org/10.1038/s41928-024-01188-y.

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Kim, Chul-Heung, Suhwan Lim, Sung Yun Woo, et al. "Emerging memory technologies for neuromorphic computing." Nanotechnology 30, no. 3 (2018): 032001. http://dx.doi.org/10.1088/1361-6528/aae975.

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Varshika, M. Lakshmi, Federico Corradi, and Anup Das. "Nonvolatile Memories in Spiking Neural Network Architectures: Current and Emerging Trends." Electronics 11, no. 10 (2022): 1610. http://dx.doi.org/10.3390/electronics11101610.

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A sustainable computing scenario demands more energy-efficient processors. Neuromorphic systems mimic biological functions by employing spiking neural networks for achieving brain-like efficiency, speed, adaptability, and intelligence. Current trends in neuromorphic technologies address the challenges of investigating novel materials, systems, and architectures for enabling high-integration and extreme low-power brain-inspired computing. This review collects the most recent trends in exploiting the physical properties of nonvolatile memory technologies for implementing efficient in-memory and

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Della Rocca, Mattia. "Of the Artistic Nude and Technological Behaviorism." Nuncius 32, no. 2 (2017): 376–411. http://dx.doi.org/10.1163/18253911-03202006.

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Neuromorphic technologies lie at the core of 21st century neuroscience, especially in the “big brain science” projects started in 2013 – i.e. the BRAIN Initiative and the Human Brain Project. While neuromorphism and the “reverse engineering” of the brain are often presented as a “methodological revolution” in the brain sciences, these concepts have a long history which is strongly interconnected with the developments in neuroscience and the related field of bioengineering since the end of World War II. In this paper I provide a short review of the first generation of “neuromorphic devices” cre

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Rajendran, Bipin, and Fabien Alibart. "Neuromorphic Computing Based on Emerging Memory Technologies." IEEE Journal on Emerging and Selected Topics in Circuits and Systems 6, no. 2 (2016): 198–211. http://dx.doi.org/10.1109/jetcas.2016.2533298.

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Woo, Jiyong, Jeong Hun Kim, Jong‐Pil Im, and Seung Eon Moon. "Recent Advancements in Emerging Neuromorphic Device Technologies." Advanced Intelligent Systems 2, no. 10 (2020): 2000111. http://dx.doi.org/10.1002/aisy.202000111.

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Woo, Jiyong, Jeong Hun Kim, Jong‐Pil Im, and Seung Eon Moon. "Recent Advancements in Emerging Neuromorphic Device Technologies." Advanced Intelligent Systems 2, no. 10 (2020): 2070101. http://dx.doi.org/10.1002/aisy.202070101.

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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 (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

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Dissertations / Theses on the topic "Neuromorphic technologies"

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Hock, Matthias [Verfasser], and Karlheinz [Akademischer Betreuer] Meier. "Modern Semiconductor Technologies for Neuromorphic Hardware / Matthias Hock ; Betreuer: Karlheinz Meier." Heidelberg : Universitätsbibliothek Heidelberg, 2014. http://d-nb.info/1180031628/34.

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Jackson, Thomas C. "Building Efficient Neuromorphic Networks in Hardware with Mixed Signal Techniques and Emerging Technologies." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/1096.

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In recent years, neuromorphic architectures have been an increasingly effective tool used to solve big data problems. Hardware neural networks have not been able to fully exploit the power efficient properties of the neural paradigm, however, due to limitations in standard CMOS. One of the largest challenges is the quadratic scaling of the synapses in a neural network. There has been some work in using post CMOS technology as synapses to overcome this limitation, but systems to date have not been scalable due to the design of their neurons. This dissertation aims to design and build scalable n

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Calayir, Vehbi. "Neurocomputing and Associative Memories Based on Emerging Technologies: Co-optimization of Technology and Architecture." Research Showcase @ CMU, 2014. http://repository.cmu.edu/dissertations/422.

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Neurocomputers offer a massively parallel computing paradigm by mimicking the human brain. Their efficient use in statistical information processing has been proposed to overcome critical bottlenecks with traditional computing schemes for applications such as image and speech processing, and associative memory. In neural networks information is generally represented by phase (e.g., oscillatory neural networks) or amplitude (e.g., cellular neural networks). Phase-based neurocomputing is constructed as a network of coupled oscillatory neurons that are connected via programmable phase elements. R

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Garbin, Daniele. "Etude de la variabilité des technologies PCM et OxRAM pour leur utilisation en tant que synapses dans les systèmes neuromorphiques." Thesis, Université Grenoble Alpes (ComUE), 2015. http://www.theses.fr/2015GREAT133/document.

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Le cerveau humain est composé d’un grand nombre de réseaux neuraux interconnectés, dont les neurones et les synapses en sont les briques constitutives. Caractérisé par une faible consommation de puissance, de quelques Watts seulement, le cerveau humain est capable d’accomplir des tâches qui sont inaccessibles aux systèmes de calcul actuels, basés sur une architecture de type Von Neumann. La conception de systèmes neuromorphiques vise à réaliser une nouvelle génération de systèmes de calcul qui ne soit pas de type Von Neumann. L’utilisation de mémoire non-volatile innovantes en tant que synapse

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Ly, Denys. "Mémoires résistives et technologies 3D monolithiques pour processeurs neuromorphiques impulsionnels et reconfigurables." Thesis, Université Grenoble Alpes, 2020. http://www.theses.fr/2020GRALT016.

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Le cerveau humain est un système computationnel complexe mais énergétiquement efficace qui excelle aux applications cognitives grâce à sa capacité naturelle à faire de l'inférence. À l'inverse, les systèmes de calculs traditionnels reposant sur la classique architecture de Von Neumann exigent des consommations de puissance importantes pour exécuter de telles tâches. Ces considérations ont donné naissance à la fameuse approche neuromorphique, qui consiste à construire des systèmes de calculs inspirés du cerveau. Dans cette thèse, nous examinons l'utilisation de technologies novatrices, à savoir

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Suri, Manan. "Technologies émergentes de mémoire résistive pour les systèmes et application neuromorphique." Phd thesis, Université de Grenoble, 2013. http://tel.archives-ouvertes.fr/tel-00935190.

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La recherche dans le domaine de l'informatique neuro-inspirée suscite beaucoup d'intérêt depuis quelques années. Avec des applications potentielles dans des domaines tels que le traitement de données à grande échelle, la robotique ou encore les systèmes autonomes intelligents pour ne citer qu'eux, des paradigmes de calcul bio-inspirés sont étudies pour la prochaine génération solutions informatiques (post-Moore, non-Von Neumann) ultra-basse consommation. Dans ce travail, nous discutons les rôles que les différentes technologies de mémoire résistive non-volatiles émergentes (RRAM), notamment (i

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Janzakova, Kamila. "Développement de dendrites polymères organiques en 3D comme dispositif neuromorphique." Electronic Thesis or Diss., Université de Lille (2022-....), 2023. http://www.theses.fr/2023ULILN017.

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Les technologies neuromorphiques constituent une voie prometteuse pour le développement d'une informatique plus avancée et plus économe en énergie. Elles visent à reproduire les caractéristiques attrayantes du cerveau, telles qu'une grande efficacité de calcul et une faible consommation d'énergie au niveau des logiciels et du matériel. À l'heure actuelle, les implémentations logicielles inspirées du cerveau (telles que ANN et SNN) ont déjà démontré leur efficacité dans différents types de tâches (reconnaissance d'images et de la parole). Toutefois, pour tirer un meilleur parti des algorithmes

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Haessig, Germain. "Neuromorphic computation using event-based sensors : from algorithms to hardware implementations." Thesis, Sorbonne université, 2018. http://www.theses.fr/2018SORUS422/document.

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Cette thèse porte sur l’implémentation d’algorithmes événementiels, en utilisant, dans un premier temps, des données provenant d’une rétine artificielle, mimant le fonctionnement de la rétine humaine, pour ensuite évoluer vers tous types de signaux événementiels. Ces signaux événementiels sont issus d’un changement de paradigme dans la représentation du signal, offrant une grande plage dynamique de fonctionnement, une résolution temporelle importante ainsi qu’une compression native du signal. Sera notamment étudiée la réalisation d’un dispositif de création de cartes de profondeur monoculaires

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Bedecarrats, Thomas. "Etude et intégration d’un circuit analogique, basse consommation et à faible surface d'empreinte, de neurone impulsionnel basé sur l’utilisation du BIMOS en technologie 28 nm FD-SOI." Thesis, Université Grenoble Alpes (ComUE), 2019. http://www.theses.fr/2019GREAT045.

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Avec la fin annoncée de la loi de Moore, les acteurs de la microélectronique cherchent de nouveaux paradigmes sur lesquels s’appuyer pour alimenter les développements futurs de notre société de l’information. En s’inspirant des systèmes nerveux biologiques, l’ingénierie neuromorphique offre des perspectives nouvelles qui révolutionnent d’ores et déjà l’intelligence artificielle. Pour que leurs performances permettent leur généralisation, les processeurs neuronaux se doivent d’intégrer des circuits de neurones les plus petits et les moins énergivores possible afin que les réseaux de neurones ar

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Cohen, Gregory Kevin. "Event-Based Feature Detection, Recognition and Classification." Thesis, Paris 6, 2016. http://www.theses.fr/2016PA066204/document.

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La detection, le suivi de cible et la reconnaissance de primitives visuelles constituent des problèmes fondamentaux de la vision robotique. Ces problématiques sont réputés difficiles et sources de défis. Malgré les progrès en puissance de calcul des machines, le gain en résolution et en fréquence des capteurs, l’état-de-l’art de la vision robotique peine à atteindre des performances en coût d’énergie et en robustesse qu’offre la vision biologique. L’apparition des nouveaux capteurs, appelés "rétines de silicium” tel que le DVS (Dynamic Vision Sensor) et l’ATIS (Asynchronous Time-based Imaging

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Books on the topic "Neuromorphic technologies"

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Pearce, Tim C. Chemosensation. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0017.

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Olfaction in animals still surpasses any technological solution to chemical sensing yet conceived. While certain classes of molecular detection technologies may be capable of high sensitivity to a restricted number of compounds, unique to the biological system is its astonishing dynamic range (over 10 orders of magnitude), combining both extreme levels of sensitivity to certain key compounds of behavioural importance and varying levels of discrimination between an almost infinite variety of ligands, presented both individually and in complex combinations. For over 30 years the olfactory system

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Book chapters on the topic "Neuromorphic technologies"

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Saïghi, Sylvain. "Neuromorphic Technologies, Memristors." In Encyclopedia of Computational Neuroscience. Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_116.

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Saïghi, Sylvain. "Neuromorphic Technologies, Memristors." In Encyclopedia of Computational Neuroscience. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-7320-6_116-1.

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Lim, Gerard Joseph, Calvin Ching Ian Ang, and Wen Siang Lew. "Spintronics for Neuromorphic Engineering." In Emerging Non-volatile Memory Technologies. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-6912-8_9.

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Dananjaya, Putu Andhita, Roshan Gopalakrishnan, and Wen Siang Lew. "RRAM-Based Neuromorphic Computing Systems." In Emerging Non-volatile Memory Technologies. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-6912-8_12.

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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. River Publishers, 2023. http://dx.doi.org/10.1201/9781003377382-1.

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Xia, Jiachen. "Application of Memristor in Neuromorphic Chips." In Smart Innovation, Systems and Technologies. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-0096-0_29.

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Li, Zheng, Chenchen Liu, Hai Li, and Yiran Chen. "Neuromorphic Hardware Acceleration Enabled by Emerging Technologies." In Emerging Technology and Architecture for Big-data Analytics. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54840-1_10.

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Molendijk, Maarten, Kanishkan Vadivel, Federico Corradi, Gert-Jan van Schaik, Amirreza Yousefzadeh, and Henk Corporaal. "Benchmarking the Epiphany Processor as a Reference Neuromorphic Architecture." In Industrial Artificial Intelligence Technologies and Applications. River Publishers, 2023. http://dx.doi.org/10.1201/9781003377382-2.

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Sengupta, Abhronil, Aayush Ankit, and Kaushik Roy. "Efficient Neuromorphic Systems and Emerging Technologies: Prospects and Perspectives." In Emerging Technology and Architecture for Big-data Analytics. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54840-1_12.

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Asad, Arghavan, and Farah Mohammadi. "NeuroTower: A 3D Neuromorphic Architecture with Low-Power TSVs." In Proceedings of the Future Technologies Conference (FTC) 2022, Volume 3. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18344-7_14.

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Conference papers on the topic "Neuromorphic technologies"

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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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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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Gupta, Shubham, Ishwar Bansal, and Geeta Geeta. "Leveraging Neuromorphic Computing for Efficient and Scalable Data Analytics." In 2025 3rd International Conference on Advancement in Computation & Computer Technologies (InCACCT). IEEE, 2025. https://doi.org/10.1109/incacct65424.2025.11011365.

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Lofù, Domenico, Paolo Sorino, Tommaso Di Noia, and Eugenio Di Sciascio. "Towards a Federated Intrusion Detection System based on Neuromorphic Computing." In 2024 9th International Conference on Smart and Sustainable Technologies (SpliTech). IEEE, 2024. http://dx.doi.org/10.23919/splitech61897.2024.10612534.

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Sun, Yang, jiayang wu, Yang Li, et al. "Ultrahigh bandwidth signal processing and neuromorphic computing based on integrated Kerr microcombs." In Integrated Optics: Devices, Materials, and Technologies XXIX, edited by Sonia M. García-Blanco and Pavel Cheben. SPIE, 2025. https://doi.org/10.1117/12.3044362.

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Diao, Xiaoguang, Yubo Song, and Subham Sahoo. "Inferring Ingrained Remote Information in AC Power Flows Using Neuromorphic Modality Regime." In 2024 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm). IEEE, 2024. http://dx.doi.org/10.1109/smartgridcomm60555.2024.10738048.

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Hüssen, Lukas, Xun Chen, Muh-Dey Wei, et al. "A DC to 5.8 GHz CMOS Variable-Gain Transimpedance Amplifier for Photonic Neuromorphic Hardware." In 2024 13th International Conference on Modern Circuits and Systems Technologies (MOCAST). IEEE, 2024. http://dx.doi.org/10.1109/mocast61810.2024.10615373.

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Uddin, S. M. Zia, Babar Khan, Syeda Aimen Naseem, and Syeda Umme Aeman Kamal. "A Review of Recent Advances in Intelligent Neuromorphic Computing-Assisted Machine Learning for Automatic Anomaly Detection." In 2024 Global Conference on Wireless and Optical Technologies (GCWOT). IEEE, 2024. https://doi.org/10.1109/gcwot63882.2024.10805682.

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Kirkland, Paul, Gaetano Di Caterina, John Soraghan, and George Matich. "Neuromorphic technologies for defence and security." In Emerging Imaging and Sensing Technologies for Security and Defence V; Advanced Manufacturing Technologies for Micro- and Nanosystems in Security and Defence III, edited by Maria Farsari, John G. Rarity, Francois Kajzar, et al. SPIE, 2020. http://dx.doi.org/10.1117/12.2575978.

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Strukov, D. "Emerging Memory Technologies for Neuromorphic Computing." In 2016 International Conference on Solid State Devices and Materials. The Japan Society of Applied Physics, 2016. http://dx.doi.org/10.7567/ssdm.2016.b-7-02.

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Reports on the topic "Neuromorphic technologies"

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Pasupuleti, Murali Krishna. AI and Quantum-Nano Frontiers: Innovations in Health, Sustainability, Energy, and Security. National Education Services, 2025. https://doi.org/10.62311/nesx/rr525.

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Abstract: This research report explores transformative advancements at the intersection of Artificial Intelligence (AI), Quantum Computing, and Nanotechnology, focusing on breakthrough innovations in health, sustainability, energy, and global security. By integrating quantum algorithms, AI-driven analytics, and advanced nanomaterials, this report highlights revolutionary solutions in precision medicine, predictive diagnostics, sustainable energy storage, universal water purification, and cybersecurity. Real-world case studies and emerging technologies such as graphene-based nanomaterials, quan

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Pasupuleti, Murali Krishna. Next-Generation Extended Reality (XR): A Unified Framework for Integrating AR, VR, and AI-driven Immersive Technologies. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv325.

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Abstract: Extended Reality (XR), encompassing Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR), is evolving into a transformative technology with applications in healthcare, education, industrial training, smart cities, and entertainment. This research presents a unified framework integrating AI-driven XR technologies with computer vision, deep learning, cloud computing, and 5G connectivity to enhance immersion, interactivity, and scalability. AI-powered neural rendering, real-time physics simulation, spatial computing, and gesture recognition enable more realistic and adap

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