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Relevant bibliographies by topics / Neuromorphic chips

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Academic literature on the topic 'Neuromorphic chips'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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Journal articles on the topic "Neuromorphic chips"

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Koch, C., and B. Mathur. "Neuromorphic vision chips." IEEE Spectrum 33, no. 5 (1996): 38–46. http://dx.doi.org/10.1109/6.490055.

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Chiang, C. T., and C. Y. Wu. "Implantable neuromorphic vision chips." Electronics Letters 40, no. 6 (2004): 361. http://dx.doi.org/10.1049/el:20040269.

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Greengard, Samuel. "Neuromorphic chips take shape." Communications of the ACM 63, no. 8 (2020): 9–11. http://dx.doi.org/10.1145/3403960.

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Fan, Luwei. "Research Progress of Neuromorphic Chips." Applied and Computational Engineering 125, no. 1 (2025): 1–7. https://doi.org/10.54254/2755-2721/2025.19928.

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The increasing amount of data in the era of artificial intelligence imposes higher demands on the computational power of neural networks, and in order to fulfill this demand, there is a pressing need to overcome the limitations imposed by the von Neumann architecture's memory wall. Memristors, with their characteristics, are considered the optimal electronic devices for implementing neuromorphic computing. Therefore, in order to better utilize memristors for the design and research of neuromorphic chips, this paper summarizes and comparatively analyzes the memristor characteristics, the RRAM b

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Satnam Singh, Ishita Sabharwal, Shweta Kushwaha, Dr. Shilpi Jain, and Dr. Madhur Jain. "Enhancing Human-Machine Interaction: Leveraging Neuromorphic Chips for Adaptive Learning and Control in Neural Prosthetics and Artificial Intelligence." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 6 (2024): 933–40. http://dx.doi.org/10.32628/cseit241061135.

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This paper examines the integration of neuromorphic chips, AI, and neural prostheses to enhance human-machine interaction. Neuromorphic chips, modelled after the brain's neural architecture, enable efficient learning, adaptive behaviour, and energy-efficient processing in AI systems and prostheses. These chips improve pattern recognition, adaptive control, and integration with the human nervous system. In neural prostheses, they promise seamless brain-computer interfaces (BCI) to restore mobility for paralyzed individuals and enable precise control of devices for people with severe disabilitie

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Merolla, Paul A., John V. Arthur, Bertram E. Shi, and Kwabena A. Boahen. "Expandable Networks for Neuromorphic Chips." IEEE Transactions on Circuits and Systems I: Regular Papers 54, no. 2 (2007): 301–11. http://dx.doi.org/10.1109/tcsi.2006.887474.

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Andreeva, N. V., V. V. Luchinin, E. A. Ryndin, et al. "Neuromorphic Memristive Chips: Design and Technology." Nano- i Mikrosistemnaya Tehnika 23, no. 6 (2021): 285–94. http://dx.doi.org/10.17587/nmst.23.285-294.

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Memristive neuromorphic chips exploit a prospective class of novel functional materials (memristors) to deploy a new architecture of spiking neural networks for developing basic blocks of brain-like systems. Memristor-based neuromorphic hardware solutions for multi-agent systems are considered as challenges in frontier areas of chip design for fast and energy-efficient computing. As functional materials, metal oxide thin films with resistive switching and memory effects (memristive structures) are recognized as a potential elemental base for new components of neuromorphic engineering, enabling

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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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Sinicin, Alexey M. "OVERVIEW OF NEUROMORPHIC CHIPS FOR ARTIFICIAL INTELLIGENCE SYSTEMS." EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA 9/9, no. 150 (2024): 85–94. http://dx.doi.org/10.36871/ek.up.p.r.2024.09.09.012.

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Neuromorphic chips are innovative computing architectures inspired by the principles of biological neural networks and promise to revolutionize the way we build energy-efficient, high-perfor-mance artificial intelligence (AI) systems. These chips offer a new way of processing data based on paral-lelism, event-driven control, and adaptive learning, which can significantly improve performance at low power consumption. This article discusses the main architectural features of neuromorphic chips, their types and areas of application, and analyzes current challenges and development prospects. The a

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Hampiholi, Narayan. "Revolutionizing AI and Computing the Neuromorphic Engineering Paradigm in Neuromorphic Chips." International Journal of Computer Trends and Technology 71, no. 1 (2024): 92–98. http://dx.doi.org/10.14445/22312803/ijctt-v72i1p115.

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Dissertations / Theses on the topic "Neuromorphic chips"

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Camilleri, Patrick [Verfasser], Bernd [Akademischer Betreuer] Michaelis, Edmund P. [Akademischer Betreuer] Burte, and Jochen [Akademischer Betreuer] Braun. "Bistable attractor dynamics in neuromorphic aVLSI chips / Patrick Camilleri. Betreuer: Bernd Michaelis ; Edmund P. Burte ; Jochen Braun." Magdeburg : Universitätsbibliothek, 2014. http://d-nb.info/1054638659/34.

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Camilleri, Patrick Verfasser], Bernd [Akademischer Betreuer] [Michaelis, Edmund P. [Akademischer Betreuer] Burte, and Jochen [Akademischer Betreuer] Braun. "Bistable attractor dynamics in neuromorphic aVLSI chips / Patrick Camilleri. Betreuer: Bernd Michaelis ; Edmund P. Burte ; Jochen Braun." Magdeburg : Universitätsbibliothek, 2014. http://d-nb.info/1054638659/34.

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Rajamanikkam, Chidhambaranathan. "Understanding Security Threats of Emerging Computing Architectures and Mitigating Performance Bottlenecks of On-Chip Interconnects in Manycore NTC System." DigitalCommons@USU, 2019. https://digitalcommons.usu.edu/etd/7453.

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Emerging computing architectures such as, neuromorphic computing and third party intellectual property (3PIP) cores, have attracted significant attention in the recent past. Neuromorphic Computing introduces an unorthodox non-von neumann architecture that mimics the abstract behavior of neuron activity of the human brain. They can execute more complex applications, such as image processing, object recognition, more efficiently in terms of performance and energy than the traditional microprocessors. However, focus on the hardware security aspects of the neuromorphic computing at its nascent sta

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Holanda, Priscila Cavalcante. "DHyANA : neuromorphic architecture for liquid computing." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2016. http://hdl.handle.net/10183/169343.

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Redes Neurais têm sido um tema de pesquisas por pelo menos sessenta anos. Desde a eficácia no processamento de informações à incrível capacidade de tolerar falhas, são incontáveis os mecanismos no cérebro que nos fascinam. Assim, não é nenhuma surpresa que, na medida que tecnologias facilitadoras tornam-se disponíveis, cientistas e engenheiros têm aumentado os esforços para o compreender e simular. Em uma abordagem semelhante à do Projeto Genoma Humano, a busca por tecnologias inovadoras na área deu origem a projetos internacionais que custam bilhões de dólares, o que alguns denominam o desper

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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 c

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Jaoudi, Yassine. "Evaluating Online Learning Anomaly Detection on Intel Neuromorphic Chip and Memristor Characterization Tool." University of Dayton / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1628082991706349.

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George, Suma. "Can my chip behave like my brain?" Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54905.

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Many decades ago, Carver Mead established the foundations of neuromorphic systems. Neuromorphic systems are analog circuits that emulate biology. These circuits utilize subthreshold dynamics of CMOS transistors to mimic the behavior of neurons. The objective is to not only simulate the human brain, but also to build useful applications using these bio-inspired circuits for ultra low power speech processing, image processing, and robotics. This can be achieved using reconfigurable hardware, like field programmable analog arrays (FPAAs), which enable configuring different applications on a cross

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Painkras, Eustace. "A chip multiprocessor for a large-scale neural simulator." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/a-chip-multiprocessor-for-a-largescale-neural-simulator(d3637073-2669-4a81-985a-2da9eec46480).html.

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A Chip Multiprocessor for a Large-scale Neural SimulatorEustace PainkrasA thesis submitted to The University of Manchesterfor the degree of Doctor of Philosophy, 17 December 2012The modelling and simulation of large-scale spiking neural networks in biologicalreal-time places very high demands on computational processing capabilities andcommunications infrastructure. These demands are difficult to satisfy even with powerfulgeneral-purpose high-performance computers. Taking advantage of the remarkableprogress in semiconductor technologies it is now possible to design and buildan application-driv

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Hasan, Md Raqibul. "Multi-core Architectures for Feed-forward Neural Networks." University of Dayton / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1395140542.

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Goshi, Sudheer. "Digital Fabric." PDXScholar, 2012. https://pdxscholar.library.pdx.edu/open_access_etds/115.

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Continuing advances with VLSI have enabled engineers to build high performance computer systems to solve complex problems. The real-world problems and tasks like pattern recognition, speech recognition, etc. still remain elusive to the most advanced computer systems today. Many advances in the science of computer design and technology are coming together to enable the creation of the next-generation computing machines to solve real-world problems, which the human brain does with ease. One such engineering advance is the field of neuromorphic engineering, which tries to establish closer links t

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Books on the topic "Neuromorphic chips"

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Dudek, Piotr. Vision. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0014.

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Vision is a sensory modality of primary importance to many animal species. The efficient implementation of visual perception is also one of the main challenges in the design of intelligent robotic systems. This chapter reviews the principles of operation and key features of the early stages of biological vision systems. Following the observation that visual information processing starts in the eye, it reviews several approaches to constructing biomimetic artificial vision systems. It presents devices inspired by the morphology of the insects’ compound eyes, and devices tightly integrating imag

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Book chapters on the topic "Neuromorphic chips"

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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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Saxena, Vishal. "Challenges and Recent Advances in NVM-based Neuromorphic Computing ICs." In Selected Topics in Intelligent Chips with Emerging Devices, Circuits and Systems. River Publishers, 2023. http://dx.doi.org/10.1201/9781003388517-6.

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Liu, Liyuan, Mingxin Zhao, Ke Ning, Xu Yang, Xuemin Zheng, and Nanjian Wu. "Neuromorphic Vision Chip." In Near-sensor and In-sensor Computing. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11506-6_5.

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Davies, Mike. "The Loihi Neuromorphic Research Chip." In From Artificial Intelligence to Brain Intelligence. River Publishers, 2022. http://dx.doi.org/10.1201/9781003338215-9.

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Eshraghian, Jason. "Open-Source RRAM Neuromorphic Accelerators." In Intelligence in Chip: Integrated Sensors and Memristive Computing. River Publishers, 2024. http://dx.doi.org/10.1201/9788770042536-15.

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Kolagasioglu, Eralp. "Single-Lead Neuromorphic ECG Classification System." In Real-Time Multi-Chip Neural Network for Cognitive Systems. River Publishers, 2022. http://dx.doi.org/10.1201/9781003339205-7.

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You, Xuefei. "Multi-Compartment Synaptic Circuit in Neuromorphic Structures." In Real-Time Multi-Chip Neural Network for Cognitive Systems. River Publishers, 2022. http://dx.doi.org/10.1201/9781003339205-8.

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Basu, Arindam. "In-memory computing meets Neuromorphic Event-Driven Vision." In Intelligence in Chip: Integrated Sensors and Memristive Computing. River Publishers, 2024. http://dx.doi.org/10.1201/9788770042536-13.

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Saxena, Vishal. "Mixed-Signal Neuromorphic Computing: Devices, Algorithms, and Circuits." In Intelligence in Chip: Integrated Sensors and Memristive Computing. River Publishers, 2024. http://dx.doi.org/10.1201/9788770042536-16.

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Merkel, Cory. "Neuromorphic Computing with Memristors: From Circuits to Algorithms." In Intelligence in Chip: Integrated Sensors and Memristive Computing. River Publishers, 2024. http://dx.doi.org/10.1201/9788770042536-12.

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Conference papers on the topic "Neuromorphic chips"

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Wang, Benshan, Qiarong Xiao, Tengji Xu, Li Fan, Shaojie Liu та Chaoran Huang. "Silicon Photonic Neuromorphic Processor for 100-Gbaud/λ Optical Communications and Beyond". У Optical Fiber Communication Conference. Optica Publishing Group, 2025. https://doi.org/10.1364/ofc.2025.tu3h.2.

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We demonstrate a programmable silicon photonic neuromorphic processor for real-time, all-optical distortion compensation of up to 100-Gbaud/λ PAM4 and OOK signals transmitted over 5 km of SMF at C-band, with latency and power consumption orders of magnitude lower than those of DSP chips.

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Titirsha, Tvisha, Md Maruf Hossain Shuvo, Shahrin Akter, and Syed Kamrul Islam. "Hill Climbing for Efficient Spiking Neural Network Acceleration on Neuromorphic Chips." In 2024 IEEE 67th International Midwest Symposium on Circuits and Systems (MWSCAS). IEEE, 2024. http://dx.doi.org/10.1109/mwscas60917.2024.10658659.

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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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Joseph, Jan Moritz, Jörn Nöller, José Cubero Cascante, and Rebecca Pelke. "Deadlocks in NoC-based Neuromorphic Systems." In 2024 17th IEEE/ACM International Workshop on Network on Chip Architectures (NoCArc). IEEE, 2024. http://dx.doi.org/10.1109/nocarc64615.2024.10749965.

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Krausse, Jann, Moritz Neher, Iris Fuerst-Walter, et al. "On Metric-Driven Development of Embedded Neuromorphic AI." In 2024 IEEE 37th International System-on-Chip Conference (SOCC). IEEE, 2024. http://dx.doi.org/10.1109/socc62300.2024.10737770.

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van de Burgt, Yoeri. "On-chip learning with organic neuromorphic systems." In Organic and Hybrid Transistors XXIII, edited by Oana D. Jurchescu and Iain McCulloch. SPIE, 2024. http://dx.doi.org/10.1117/12.3028107.

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Goel, Akash, Alok Katiyar, Amit Kumar Goel, and Adesh Kumar. "LSTM Neural Networks for Brain Signals and Neuromorphic Chip." In 2024 2nd International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT). IEEE, 2024. https://doi.org/10.1109/icaiccit64383.2024.10912358.

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Van De Burgt, Yoeri. "Recent Advances in on-Chip Learning with Organic Neuromorphic Circuits." In 2025 9th IEEE Electron Devices Technology & Manufacturing Conference (EDTM). IEEE, 2025. https://doi.org/10.1109/edtm61175.2025.11041659.

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Langer, Tim, Matthias Jobst, Chen Liu, Florian Kelber, Bernhard Vogginger, and Christian Mayr. "OctopuScheduler: On-Chip DNN Scheduling on the SpiNNaker2 Neuromorphic MPSoC." In 2025 Neuro Inspired Computational Elements (NICE). IEEE, 2025. https://doi.org/10.1109/nice65350.2025.11065866.

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Chen, Xin-Ping, Hsu-Yu Huang, Chu-Yun Hsiao, Jennifer Shueh-Inn Hu, and James Chien-Mo Li. "Test Compression for Neuromorphic Chips." In 2024 IEEE European Test Symposium (ETS). IEEE, 2024. http://dx.doi.org/10.1109/ets61313.2024.10567560.

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Reports on the topic "Neuromorphic chips"

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Pasupuleti, Murali Krishna. Neuromorphic Nanotech: 2D Materials for Energy-Efficient Edge Computing. National Education Services, 2025. https://doi.org/10.62311/nesx/rr325.

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Abstract The demand for energy-efficient, real-time computing is driving the evolution of neuromorphic computing and edge AI systems. Traditional silicon-based processors struggle with power inefficiencies, memory bottlenecks, and scalability limitations, making them unsuitable for next-generation low-power AI applications. This research report explores how 2D materials, such as graphene, transition metal dichalcogenides (TMDs), black phosphorus, and MXenes, are enabling the development of neuromorphic architectures that mimic biological neural networks for high-speed, ultra-low-power computat

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