Relevant bibliographies by topics / Neuromorphic technologies/devices

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

Academic literature on the topic 'Neuromorphic technologies/devices'

Author: Grafiati
Published: 15 June 2024
Last updated: 31 July 2025

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Journal articles on the topic "Neuromorphic technologies/devices"

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Orii, Yasumitsu, Akihiro Horibe, Kuniaki Sueoka, et al. "PERSPECTIVE ON REQUIRED PACKAGING TECHNOLOGIES FOR NEUROMORPHIC DEVICES." International Symposium on Microelectronics 2015, no. 1 (2015): 000561–66. http://dx.doi.org/10.4071/isom-2015-tha15.

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Cognitive computing has capability of machine learning, recognition and proposal. It is essential to make human life richer, more productive and more intelligent. For the realization of the cognitive computing, an efficient and scalable non-von Neumann architecture inspired by the human brain structure has been developed and a device which demonstrates the concept was also built. This device mimics the signal processing of the human brain, packing one million neuron circuits in 4,096 cores. It consumes almost 1,000 times less energy per event compared with a state-of-the-art multiprocessor. Ho
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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 (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 heterostructure
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Milo, Valerio, Gerardo Malavena, Christian Monzio Compagnoni, and Daniele Ielmini. "Memristive and CMOS Devices for Neuromorphic Computing." Materials 13, no. 1 (2020): 166. http://dx.doi.org/10.3390/ma13010166.

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Neuromorphic computing has emerged as one of the most promising paradigms to overcome the limitations of von Neumann architecture of conventional digital processors. The aim of neuromorphic computing is to faithfully reproduce the computing processes in the human brain, thus paralleling its outstanding energy efficiency and compactness. Toward this goal, however, some major challenges have to be faced. Since the brain processes information by high-density neural networks with ultra-low power consumption, novel device concepts combining high scalability, low-power operation, and advanced comput
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Jha, Rashmi, Vamshi Kiran Kiran Gogi, and Siddharth Barve. "(Invited) Novel Neuromorphic Computing Paradigms Enabled By Emerging Memory Devices." ECS Meeting Abstracts MA2024-01, no. 57 (2024): 3011. http://dx.doi.org/10.1149/ma2024-01573011mtgabs.

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Implementation of Artificial Intelligence and Machine Learning algorithms on conventional Von Neumann computing architectures are crippled by the memory-wall bottleneck. To overcome these issues, novel computing architectures with high-bandwidth memories, in-memory computing, and near-memory computing capabilities are being developed. Almost all of these architectures will benefit from high-density on-chip non-volatile memories, offered by the emerging non-volatile memory devices. Additionally, emerging memory devices offer rich device physics that can be leveraged for the implementation of no
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Huang, Tianci, Yuxuan Wang, Zhihan Jin, et al. "A Review of Nanowire Devices Applied in Simulating Neuromorphic Computing." Nanomaterials 15, no. 10 (2025): 724. https://doi.org/10.3390/nano15100724.

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With the rapid advancement of artificial intelligence and machine learning technologies, the demand for enhanced device computing capabilities has significantly increased. Neuromorphic computing, an emerging computational paradigm inspired by the human brain, has garnered growing attention as a promising research frontier. Inspired by the human brain’s functionality, this technology mimics the behavior of neurons and synapses to enable efficient, low-power computing. Unlike conventional digital systems, this approach offers a potentially superior alternative. This article delves into the appli
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Meng, Xiaohan, Runsheng Gao, Xiaojian Zhu, and Run-Wei Li. "Ion-modulation optoelectronic neuromorphic devices: mechanisms, characteristics, and applications." Journal of Semiconductors 46, no. 2 (2025): 021402. https://doi.org/10.1088/1674-4926/24100025.

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Abstract The traditional von Neumann architecture faces inherent limitations due to the separation of memory and computation, leading to high energy consumption, significant latency, and reduced operational efficiency. Neuromorphic computing, inspired by the architecture of the human brain, offers a promising alternative by integrating memory and computational functions, enabling parallel, high-speed, and energy-efficient information processing. Among various neuromorphic technologies, ion-modulated optoelectronic devices have garnered attention due to their excellent ionic tunability and the
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Kim, So-Yeon. "Operating Mechanism Principles and Advancements for Halide Perovskite-Based Memristors and Neuromorphic Devices." Journal of Physical Chemistry Letters 15 (September 30, 2024): 10087–103. https://doi.org/10.1021/acs.jpclett.4c02170.

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With the advent of the generation of artificial intelligence (AI) based on big data-processing technologies, next-generation memristor and memristive neuromorphic devices have been actively studied with great interest to overcome the von Neumann bottleneck limits. Among various candidates, halide perovskites (HPs) have been in the spotlight as potential candidates for these devices due to their unique switching characteristics with low energy consumption and flexible integration compatibility across various sources for scalability. We outline the characteristics and operating principles of HP-
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Abbas, Haider, Jiayi Li, and Diing Shenp Ang. "Conductive Bridge Random Access Memory (CBRAM): Challenges and Opportunities for Memory and Neuromorphic Computing Applications." Micromachines 13, no. 5 (2022): 725. http://dx.doi.org/10.3390/mi13050725.

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Due to a rapid increase in the amount of data, there is a huge demand for the development of new memory technologies as well as emerging computing systems for high-density memory storage and efficient computing. As the conventional transistor-based storage devices and computing systems are approaching their scaling and technical limits, extensive research on emerging technologies is becoming more and more important. Among other emerging technologies, CBRAM offers excellent opportunities for future memory and neuromorphic computing applications. The principles of the CBRAM are explored in depth
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Allwood, Dan A., Matthew O. A. Ellis, David Griffin, et al. "A perspective on physical reservoir computing with nanomagnetic devices." Applied Physics Letters 122, no. 4 (2023): 040501. http://dx.doi.org/10.1063/5.0119040.

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Neural networks have revolutionized the area of artificial intelligence and introduced transformative applications to almost every scientific field and industry. However, this success comes at a great price; the energy requirements for training advanced models are unsustainable. One promising way to address this pressing issue is by developing low-energy neuromorphic hardware that directly supports the algorithm's requirements. The intrinsic non-volatility, non-linearity, and memory of spintronic devices make them appealing candidates for neuromorphic devices. Here, we focus on the reservoir c
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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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Dissertations / Theses on the topic "Neuromorphic technologies/devices"

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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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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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Book chapters on the topic "Neuromorphic technologies/devices"

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Ricci, Saverio, Piergiulio Mannocci, Matteo Farronato, Alessandro Milozzi, and Daniele Ielmini. "Development of Crosspoint Memory Arrays for Neuromorphic Computing." In Special Topics in Information Technology. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-51500-2_6.

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AbstractMemristor-based hardware accelerators play a crucial role in achieving energy-efficient big data processing and artificial intelligence, overcoming the limitations of traditional von Neumann architectures. Resistive-switching memories (RRAMs) combine a simple two-terminal structure with the possibility of tuning the device conductance. This Chapter revolves around the topic of emerging memristor-related technologies, starting from their fabrication, through the characterization of single devices up to the development of proof-of-concept experiments in the field of in-memory computing,
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Carstens, Niko, Maik-Ivo Terasa, Pia Holtz, et al. "Memristive Switching: From Individual Nanoparticles Towards Complex Nanoparticle Networks." In Springer Series on Bio- and Neurosystems. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-36705-2_9.

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AbstractNovel hardware concepts in the framework of neuromorphic engineering are intended to overcome fundamental limits of current computer technologies and to be capable of efficient mass data processing. To reach this, research into material systems which enable the implementation of memristive switching in electronic devices, as well as into analytical approaches helping to understand fundamental mechanisms and dynamics of memristive switching is inevitable. In this chapter, memristive switching based on Ag metal filament formation is discussed throughout different scales, providing insigh
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Walters, B., C. Lammie, J. Eshraghian, et al. "Memristive Devices for Neuromorphic and Deep Learning Applications." In Advanced Memory Technology. Royal Society of Chemistry, 2023. http://dx.doi.org/10.1039/bk9781839169946-00680.

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Neuromorphic and deep learning (DL) algorithms are important research areas gaining significant traction of late. Due to this growing interest and the high demand for low-power and high-performance designs for running these algorithms, various circuits and devices are being designed and investigated to realize efficient neuromorphic and DL architectures. One device said to drastically improve this architecture is the memristor. In this chapter, studies investigating memristive implementations into neuromorphic and DL designs are summarized and categorized based on the switching mechanicsms of
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Anil Chandra, G. V. S., Bhanuprakash Ananthakumar, and Ramya Raghavan. "Enhancing Assistive Technologies With Neuromorphic Computing." In Advances in Computational Intelligence and Robotics. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-6303-4.ch009.

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The development of intelligent neuroprosthetics, which promise to augment human brain function is vital for augmentative assistive technologies. Neuromorphic sensors and processors are particularly adept at mimicking the brain's efficient sensory processing, offering assistive devices an advanced capability to perceive and interpret complex environmental stimuli. The application of these technologies in brain computer interfaces suggests a future where transformative advancements are not only possible but imminent, facilitating novel methods of human-computer interaction and providing insights
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Shanbogh, Shobith M., R. Anju Kumari, and Ponnam Anjaneyulu. "Hybrid Devices for Neuromorphic Applications." In Advanced Memory Technology. Royal Society of Chemistry, 2023. http://dx.doi.org/10.1039/bk9781839169946-00622.

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The world always seeks new materials, devices and technologies for a better future, and thus researchers keep exploring the possibilities. Advanced memory technology also aims to make the world better, comfortable, accessible and explorable. In this direction, hybrid devices consisting of dissimilar materials stacked or fused together can be considered as propitious. An attempt is made to identify the advantages of hybrid structures by implementing them into new memory technology architectures. Hybrid device structures including organic–inorganic, inorganic–inorganic (with different dimensions
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Kamble, Girish U., Chandrashekhar S. Patil, Vidya V. Alman, Somnath S. Kundale, and Jin Hyeok Kim. "Neuromorphic Computing: Cutting-Edge Advances and Future Directions." In Recent Advances in Neuromorphic Computing [Working Title]. IntechOpen, 2024. http://dx.doi.org/10.5772/intechopen.1006712.

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Neuromorphic computing draws motivation from the human brain and presents a distinctive substitute for the traditional von Neumann architecture. Neuromorphic systems provide simultaneous data analysis, energy efficiency, and error resistance by simulating neural networks. They promote innovations in eHealth, science, education, transportation, smart city planning, and the metaverse, spurred on by deep learning and artificial intelligence. However, performance-focused thinking frequently ignores sustainability, emphasizing the need for harmony. Three primary domains comprise neuromorphic resear
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Yang, Chaofei, Hai Li, and Yiran Chen. "Nanoscale Memory Architectures for Neuromorphic Computing." In Security Opportunities in Nano Devices and Emerging Technologies. CRC Press, 2017. http://dx.doi.org/10.1201/9781315265056-12.

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Ahmed, T., V. Krishnamurthi, and S. Walia. "Working Dynamics in Low-dimensional Material-based Neuromorphic Devices." In Advanced Memory Technology. Royal Society of Chemistry, 2023. http://dx.doi.org/10.1039/bk9781839169946-00458.

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The exotic properties of low-dimensional materials have enabled brain-inspired computation to be unprecedently achieved in a variety of electronic and optoelectronic devices. With a plethora of highly efficient memory devices and architectures being developed lately for neuromorphic engineering and technology, the question of what types of materials and physical mechanisms will be used in futuristic neuromorphic devices is still open-ended. For this reason, a holistic understanding of the underlaying working dynamics is highly imperative to proceed forward. In this chapter, we present an overv
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Ahmed, L. Jubair, S. Dhanasekar, K. Martin Sagayam, et al. "Introduction to Neuromorphic Computing Systems." In Advances in Systems Analysis, Software Engineering, and High Performance Computing. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-6596-7.ch001.

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The process of using electronic circuits to replicate the neurobiological architectures seen in the nervous system is known as neuromorphic engineering, also referred to as neuromorphic computing. These technologies are essential for the future of computing, although most of the work in neuromorphic computing has been focused on hardware development. The execution speed, energy efficiency, accessibility and robustness against local failures are vital advantages of neuromorphic computing over conventional methods. Spiking neural networks are generated using neuromorphic computing. This chapter
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Pandey, Devendra G., Yogesh Kumar Sharma, and Nimish Kumar. "Neuromorphic Computing." In Advances in Computational Intelligence and Robotics. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-6303-4.ch017.

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The exponential growth of data and information has stimulated technological progress in computing systems that utilize them to effectively discover patterns and produce important insights. Neural network algorithms have been applied to conventional silicon transistor-based hardware to do highly parallel computations, drawing inspiration from the structure and functions of biological synapses and neurons in the brain. Nevertheless, synapses composed of many transistors are limited to storing binary data, and the utilization of intricate silicon neuron circuits to handle these digital states pos
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Conference papers on the topic "Neuromorphic technologies/devices"

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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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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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Shelby, Robert M., Pritish Narayanan, Stefano Ambrogio, et al. "Neuromorphic technologies for next-generation cognitive computing." In 2017 IEEE Electron Devices Technology and Manufacturing Conference (EDTM). IEEE, 2017. http://dx.doi.org/10.1109/edtm.2017.7947500.

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Lee, Sungsik. "Amorphous oxide thin-film devices for neuromorphic applications." In Advances in Display Technologies XII, edited by Jiun-Haw Lee, Qiong-Hua Wang, and Tae-Hoon Yoon. SPIE, 2022. http://dx.doi.org/10.1117/12.2612015.

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Offrein, Bert Jan, Tommaso Stecconi, Donato Francesco Falcone, et al. "Photonic and electronic integrated technologies for neuromorphic computing." In 2023 International Conference on Solid State Devices and Materials. The Japan Society of Applied Physics, 2023. http://dx.doi.org/10.7567/ssdm.2023.h-2-01.

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Shastri, Bhavin J., Thomas Ferreira de Lima, Alexander N. Tait, et al. "Advances in neuromorphic photonics (Conference Presentation)." In Integrated Optics: Devices, Materials, and Technologies XXIV, edited by Sonia M. García-Blanco and Pavel Cheben. SPIE, 2020. http://dx.doi.org/10.1117/12.2554476.

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Dabos, George, George Mourgias-Alexandris, Angelina Totovic, et al. "End-to-end deep learning with neuromorphic photonics." In Integrated Optics: Devices, Materials, and Technologies XXV, edited by Sonia M. García-Blanco and Pavel Cheben. SPIE, 2021. http://dx.doi.org/10.1117/12.2587668.

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Polnau, Ernst E., and Mikhail Vorontsov. "Atmospheric turbulence characterization using a neuromorphic camera." In Image Sensing Technologies: Materials, Devices, Systems, and Applications IX, edited by K. Kay Son, Nibir K. Dhar, Achyut K. Dutta, and Sachidananda R. Babu. SPIE, 2022. http://dx.doi.org/10.1117/12.2618894.

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Phillips, Matthew E., Nigel D. Stepp, Jose Cruz-Albrecht, Vincent De Sapio, Tsai-Ching Lu, and Vincent Sritapan. "Neuromorphic and early warning behavior-based authentication for mobile devices." In 2016 IEEE Symposium on Technologies for Homeland Security (HST). IEEE, 2016. http://dx.doi.org/10.1109/ths.2016.7568965.

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Yoo, S. J. Ben. "Intelligent imaging microsystems realized by 3D electronic-photonic integrated circuits with embedded neuromorphic computing." In Image Sensing Technologies: Materials, Devices, Systems, and Applications XI, edited by Nibir K. Dhar, Achyut K. Dutta, and Sachidananda R. Babu. SPIE, 2024. http://dx.doi.org/10.1117/12.3013294.

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