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Artykuły w czasopismach na temat „Neuromorphic applications”

Autor: Grafiati

Data publikacji: 6 września 2023

Data aktualizacji: 26 lipca 2025

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1

Bi, Jinming, Yanran Li, Rong Lu, Honglin Song, and Jie Jiang. "Electrolyte-gated optoelectronic transistors for neuromorphic applications." Journal of Semiconductors 46, no. 2 (2025): 021401. https://doi.org/10.1088/1674-4926/24090042.

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Abstract The traditional von Neumann architecture has demonstrated inefficiencies in parallel computing and adaptive learning, rendering it incapable of meeting the growing demand for efficient and high-speed computing. Neuromorphic computing with significant advantages such as high parallelism and ultra-low power consumption is regarded as a promising pathway to overcome the limitations of conventional computers and achieve the next-generation artificial intelligence. Among various neuromorphic devices, the artificial synapses based on electrolyte-gated transistors stand out due to their low

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Park, Jisoo, Jihyun Shin, and Hocheon Yoo. "Heterostructure-Based Optoelectronic Neuromorphic Devices." Electronics 13, no. 6 (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 expl

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Schuman, Catherine. "(Invited) Application-Hardware Co-Design for Neuromorphic Computing Systems." ECS Meeting Abstracts MA2025-01, no. 63 (2025): 3082. https://doi.org/10.1149/ma2025-01633082mtgabs.

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Neuromorphic computing offers the opportunity for low-power, intelligent autonomous systems. However, effectively leveraging neuromorphic computers requires co-design of hardware, algorithms, and applications. In this talk, I will review our recent work on hardware-application co-design in neuromorphic computing. I will discuss co-design results with a variety of neuromorphic devices, including memristors and ferroelectric devices. I will also present several applications of neuromorphic computing, including autonomous vehicles and internal combustion engine control.

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Mikki, Said. "Generalized Neuromorphism and Artificial Intelligence: Dynamics in Memory Space." Symmetry 16, no. 4 (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 o

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5

Henkel, Jorg. "Stochastic Computing for Neuromorphic Applications." IEEE Design & Test 38, no. 6 (2021): 4. http://dx.doi.org/10.1109/mdat.2021.3126288.

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Wang, Weisheng, and Liqiang Zhu. "Electrolyte Gated Transistors for Brain Inspired Neuromorphic Computing and Perception Applications: A Review." Nanomaterials 15, no. 5 (2025): 348. https://doi.org/10.3390/nano15050348.

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Emerging neuromorphic computing offers a promising and energy-efficient approach to developing advanced intelligent systems by mimicking the information processing modes of the human brain. Moreover, inspired by the high parallelism, fault tolerance, adaptability, and low power consumption of brain perceptual systems, replicating these efficient and intelligent systems at a hardware level will endow artificial intelligence (AI) and neuromorphic engineering with unparalleled appeal. Therefore, construction of neuromorphic devices that can simulate neural and synaptic behaviors are crucial for a

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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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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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9

Schuman, Catherine, Robert Patton, Shruti Kulkarni, et al. "Evolutionary vs imitation learning for neuromorphic control at the edge*." Neuromorphic Computing and Engineering 2, no. 1 (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 a

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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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Shao, Jiale, Hongwei Ying, Peihong Cheng, et al. "Artificial sensory neurons and their applications." Journal of Semiconductors 46, no. 1 (2025): 011606. https://doi.org/10.1088/1674-4926/24080039.

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Abstract With the rapid development of artificial intelligence (AI) technology, the demand for high-performance and energy-efficient computing is increasingly growing. The limitations of the traditional von Neumann computing architecture have prompted researchers to explore neuromorphic computing as a solution. Neuromorphic computing mimics the working principles of the human brain, characterized by high efficiency, low energy consumption, and strong fault tolerance, providing a hardware foundation for the development of new generation AI technology. Artificial neurons and synapses are the two

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Chature, Anjali, A. Raganna, and Venkateshappa Venkateshappa. "Study on neuromorphic computation and its applications." Indonesian Journal of Electrical Engineering and Computer Science 39, no. 1 (2025): 272. https://doi.org/10.11591/ijeecs.v39.i1.pp272-282.

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Neuromorphic computing offers a promising alternative to traditional von Neumann architectures, especially for applications that require efficient processing in edge environments. The challenge lies in optimizing spiking neural networks (SNNs) for these environments to achieve high computational efficiency, particularly in event-driven applications. This paper investigates the integration of advanced simulation tools, such as Simeuro and SuperNeuro, to enhance SNN performance on edge devices. Through comprehensive studies of various SNN models, a novel SNN design with optimized hardware compon

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Huang, Heyi, Chen Ge, Zhuohui Liu, et al. "Electrolyte-gated transistors for neuromorphic applications." Journal of Semiconductors 42, no. 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 (2020): 854–56. http://dx.doi.org/10.1016/j.eng.2020.06.010.

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Lv, Wenxing, Jialin Cai, Huayao Tu, et al. "Stochastic artificial synapses based on nanoscale magnetic tunnel junction for neuromorphic applications." Applied Physics Letters 121, no. 23 (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-s

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16

Marquez, Bicky A., Matthew J. Filipovich, Emma R. Howard, et al. "Silicon photonics for artificial intelligence applications." Photoniques, no. 104 (September 2020): 40–44. http://dx.doi.org/10.1051/photon/202010440.

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Artificial intelligence enabled by neural networks has enabled applications in many fields (e.g. medicine, finance, autonomous vehicles). Software implementations of neural networks on conventional computers are limited in speed and energy efficiency. Neuromorphic engineering aims to build processors in which hardware mimic neurons and synapses in brain for distributed and parallel processing. Neuromorphic engineering enabled by silicon photonics can offer subnanosecond latencies, and can extend the domain of artificial intelligence applications to high-performance computing and ultrafast lear

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17

Wang, Ye-Guo. "Applications of Memristors in Neural Networks and Neuromorphic Computing: A Review." International Journal of Machine Learning and Computing 11, no. 5 (2021): 350–56. http://dx.doi.org/10.18178/ijmlc.2021.11.5.1060.

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Odumeru, Abiola Odutayo. "Simulating Neuromorphic Behavior in Memory Devices with Special Ions: Insights into Device Performance and Predictive Modeling." International Journal of Advances in Engineering and Management 7, no. 1 (2025): 80–84. https://doi.org/10.35629/5252-07018084.

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The development of neuromorphic memory, which mimics the computational processes of the brain, offers exciting prospects for applications in advanced computing. This studyassesses the potential of metal-ion-containing polyvinyl acetate (PVAc) fiber as neuromorphic memory devices. To assess the synaptic plasticity and hysteresis characteristics of the manufactured devices, they underwent a thorough electrical characterization process that included currentvoltage (I-V) testing and pulse stress analysis. With long-term depression (LTD) under negative stress (-6V) and long-term potentiation (LTP)

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Huang, Yi, Fatemeh Kiani, Fan Ye, and Qiangfei Xia. "From memristive devices to neuromorphic systems." Applied Physics Letters 122, no. 11 (2023): 110501. http://dx.doi.org/10.1063/5.0133044.

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Progress in hardware and algorithms for artificial intelligence (AI) has ushered in large machine learning models and various applications impacting our everyday lives. However, today's AI, mainly artificial neural networks, still cannot compete with human brains because of two major issues: the high energy consumption of the hardware running AI models and the lack of ability to generalize knowledge and self-adapt to changes. Neuromorphic systems built upon emerging devices, for instance, memristors, provide a promising path to address these issues. Although innovative memristor devices and ci

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Tyler, Neil. "Tempo Targets Low-Power Chips for AI Applications." New Electronics 52, no. 13 (2019): 7. http://dx.doi.org/10.12968/s0047-9624(22)61557-8.

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Jué, Emilie, Matthew R. Pufall, Ian W. Haygood, William H. Rippard, and Michael L. Schneider. "Perspectives on nanoclustered magnetic Josephson junctions as artificial synapses." Applied Physics Letters 121, no. 24 (2022): 240501. http://dx.doi.org/10.1063/5.0118287.

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A nanoclustered magnetic Josephson junction (nMJJ) is a hybrid magnetic-superconducting device that can be used as an artificial synapse in neuromorphic applications. In this paper, we review the nMJJ from the device level to the circuit level. We describe the properties of individual devices and show how they can be integrated into a neuromorphic circuit. We discuss the current limitations related to the study of the nMJJ, what can be done to improve the device and better understand the underlying physics, and where the community can focus its efforts to develop magnetic Josephson junctions f

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Xu, Jiaqi, Xiaoning Zhao, Xiaoli Zhao, et al. "Memristors with Biomaterials for Biorealistic Neuromorphic Applications." Small Science 2, no. 10 (2022): 2270020. http://dx.doi.org/10.1002/smsc.202270020.

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Schuman, Catherine D., Shruti R. Kulkarni, Maryam Parsa, J. Parker Mitchell, Prasanna Date, and Bill Kay. "Opportunities for neuromorphic computing algorithms and applications." Nature Computational Science 2, no. 1 (2022): 10–19. http://dx.doi.org/10.1038/s43588-021-00184-y.

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Hajtó, Dániel, Ádám Rák, and György Cserey. "Robust Memristor Networks for Neuromorphic Computation Applications." Materials 12, no. 21 (2019): 3573. http://dx.doi.org/10.3390/ma12213573.

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One of the main obstacles for memristors to become commonly used in electrical engineering and in the field of artificial intelligence is the unreliability of physical implementations. A non-uniform range of resistance, low mass-production yield and high fault probability during operation are disadvantages of the current memristor technologies. In this article, the authors offer a solution for these problems with a circuit design, which consists of many memristors with a high operational variance that can form a more robust single memristor. The proposition is confirmed by physical device meas

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Du, Junmei, Bai Sun, Chuan Yang, et al. "Ferroelectric memristor and its neuromorphic computing applications." Materials Today Physics 50 (January 2025): 101607. https://doi.org/10.1016/j.mtphys.2024.101607.

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Erokhin, Victor. "Memristive Devices for Neuromorphic Applications: Comparative Analysis." BioNanoScience 10, no. 4 (2020): 834–47. http://dx.doi.org/10.1007/s12668-020-00795-1.

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Park, Jeongwon. "(Invited) Perspectives and Opportunities for Neuromorphic Computing and Engineering." ECS Meeting Abstracts MA2025-01, no. 63 (2025): 3081. https://doi.org/10.1149/ma2025-01633081mtgabs.

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Neuromorphic computing is set to influence the future of computing significantly. While much research has focused on hardware improvements, this presentation highlights recent advancements in neuromorphic computing and its applications. We underscore the attributes that make neuromorphic technologies attractive for future computing requirements and examine opportunities for further algorithm and application development within these systems. As Moore's law approaches its limits and Dennard scaling concludes, the computing community actively seeks new technologies to maintain performance growth.

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Al Abdul Wahid, Seham, Arghavan Asad, and Farah Mohammadi. "A Survey on Neuromorphic Architectures for Running Artificial Intelligence Algorithms." Electronics 13, no. 15 (2024): 2963. http://dx.doi.org/10.3390/electronics13152963.

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Neuromorphic computing, a brain-inspired non-Von Neumann computing system, addresses the challenges posed by the Moore’s law memory wall phenomenon. It has the capability to enhance performance while maintaining power efficiency. Neuromorphic chip architecture requirements vary depending on the application and optimising it for large-scale applications remains a challenge. Neuromorphic chips are programmed using spiking neural networks which provide them with important properties such as parallelism, asynchronism, and on-device learning. Widely used spiking neuron models include the Hodgkin–Hu

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Guo, Zhonghao. "Synaptic device-based neuromorphic computing in artificial intelligence." Applied and Computational Engineering 65, no. 1 (2024): 253–59. http://dx.doi.org/10.54254/2755-2721/65/20240511.

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The application of synaptic device-based neuromorphic computing in artificial intelligence is an emerging research field aimed at simulating the structure and function of the human brain and realizing high-efficiency, low-power, and adaptive intelligent computing. This paper reviews the principles, growth and challenges of neuromorphic devices based on synapses computing and its applications and perspectives in artificial intelligence fields like an image processing as well as natural language processing. The paper first introduces the basic concepts, properties and classification of synaptic

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Li, Sheng, Lin Gao, Changjian Liu, Haihong Guo, and Junsheng Yu. "Biomimetic Neuromorphic Sensory System via Electrolyte Gated Transistors." Sensors 24, no. 15 (2024): 4915. http://dx.doi.org/10.3390/s24154915.

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Biomimetic neuromorphic sensing systems, inspired by the structure and function of biological neural networks, represent a major advancement in the field of sensing technology and artificial intelligence. This review paper focuses on the development and application of electrolyte gated transistors (EGTs) as the core components (synapses and neuros) of these neuromorphic systems. EGTs offer unique advantages, including low operating voltage, high transconductance, and biocompatibility, making them ideal for integrating with sensors, interfacing with biological tissues, and mimicking neural proc

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Li, Tongxuan. "Neuromorphic Devices Based on Two-Dimensional Materials and Their Applications." Highlights in Science, Engineering and Technology 87 (March 26, 2024): 186–91. http://dx.doi.org/10.54097/kxsmsn90.

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Neuromorphic computing, inspired by the human brain, utilizes thin 2D materials like graphene for their unique electronic properties. These materials are crucial in creating efficient, high-performance computing devices. This paper discusses the synthesis methods for 2D materials, including chemical vapor deposition and mechanical exfoliation, and their integration into neuromorphic device architectures such as transistors and memristors. The paper explores how these devices emulate synaptic behaviors and neuronal activities through charge transport mechanisms, ion migration, and the exploitat

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J, Muralidharan, Srinivasa Rao B, Davinder Kumar, and Lakshmi Narayana T. "EXPLORING NEUROMORPHIC COMPUTING IN VLSI FOR EFFICIENT AI INFERENCE." ICTACT Journal on Microelectronics 9, no. 3 (2023): 1620–27. https://doi.org/10.21917/ijme.2023.0281.

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In artificial intelligence (AI), the demand for efficient and accelerated inference processes has spurred the exploration of neuromorphic computing paradigms implemented in Very Large Scale Integration (VLSI) systems. This study addresses the escalating need for energy-efficient and high-performance AI inference solutions by delving into the potential of neuromorphic VLSI architectures. As AI applications proliferate, traditional computing architectures face challenges in meeting the burgeoning computational demands while maintaining energy efficiency. Neuromorphic computing, inspired by the h

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Martins, Raquel Azevedo, Emanuel Carlos, Jonas Deuermeier, et al. "Emergent solution based IGZO memristor towards neuromorphic applications." Journal of Materials Chemistry C 10, no. 6 (2022): 1991–98. http://dx.doi.org/10.1039/d1tc05465a.

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Blachowicz, Tomasz, and Andrea Ehrmann. "Magnetic Elements for Neuromorphic Computing." Molecules 25, no. 11 (2020): 2550. http://dx.doi.org/10.3390/molecules25112550.

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Neuromorphic computing is assumed to be significantly more energy efficient than, and at the same time expected to outperform, conventional computers in several applications, such as data classification, since it overcomes the so-called von Neumann bottleneck. Artificial synapses and neurons can be implemented into conventional hardware using new software, but also be created by diverse spintronic devices and other elements to completely avoid the disadvantages of recent hardware architecture. Here, we report on diverse approaches to implement neuromorphic functionalities in novel hardware usi

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Lu, Shize, and Xinqing Xiao. "Neuromorphic Computing for Smart Agriculture." Agriculture 14, no. 11 (2024): 1977. http://dx.doi.org/10.3390/agriculture14111977.

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Neuromorphic computing has received more and more attention recently since it can process information and interact with the world like the human brain. Agriculture is a complex system that includes many processes of planting, breeding, harvesting, processing, storage, logistics, and consumption. Smart devices in association with artificial intelligence (AI) robots and Internet of Things (IoT) systems have been used and also need to be improved to accommodate the growth of computing. Neuromorphic computing has a great potential to promote the development of smart agriculture. The aim of this pa

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Park, Sungmin, Muhammad Naqi, Namgyu Lee, Suyoung Park, Seongin Hong, and Byeong Hyeon Lee. "Recent Advancements in 2D Material-Based Memristor Technology Toward Neuromorphic Computing." Micromachines 15, no. 12 (2024): 1451. http://dx.doi.org/10.3390/mi15121451.

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Two-dimensional (2D) layered materials have recently gained significant attention and have been extensively studied for their potential applications in neuromorphic computing, where they are used to mimic the functions of the human brain. Their unique properties, including atomic-level thickness, exceptional mechanical stability, and tunable optical and electrical characteristics, make them highly versatile for a wide range of applications. In this review, we offer a comprehensive analysis of 2D material-based memristors. Furthermore, we examine the ability of 2D material-based memristors to s

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Elitalib, Elmunazir Husein, and Asnidar A. Ani Bahar. "Neuromorphic Computing Architectures for Real-time Image Processing and Pattern Recognition." Algorithm Asynchronous 1, no. 1 (2023): 24–32. http://dx.doi.org/10.61963/jaa.v1i1.48.

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Real-time image processing and pattern recognition applications have found a new paradigm in neuromorphic computing systems. In this paper, we quantitatively compare neuromorphic architecture performance to that of conventional computing techniques. We study processing speed, accuracy, and energy usage for diverse image processing jobs using a controlled experimental methodology. The outcomes highlight the advantages of the Neuromorphic architecture, which is distinguished by quicker processing times and greater precision. These results demonstrate the effectiveness of event-driven spiking neu

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Feng, Chenyin, Wenwei Wu, Huidi Liu, et al. "Emerging Opportunities for 2D Materials in Neuromorphic Computing." Nanomaterials 13, no. 19 (2023): 2720. http://dx.doi.org/10.3390/nano13192720.

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Recently, two-dimensional (2D) materials and their heterostructures have been recognized as the foundation for future brain-like neuromorphic computing devices. Two-dimensional materials possess unique characteristics such as near-atomic thickness, dangling-bond-free surfaces, and excellent mechanical properties. These features, which traditional electronic materials cannot achieve, hold great promise for high-performance neuromorphic computing devices with the advantages of high energy efficiency and integration density. This article provides a comprehensive overview of various 2D materials,

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Moon, Jaehyun, Ju-Hun Lee, Kitae Kim, et al. "Threshold Switching of ALD-NbO_x Films for Neuromorphic Applications." ECS Meeting Abstracts MA2023-02, no. 30 (2023): 1558. http://dx.doi.org/10.1149/ma2023-02301558mtgabs.

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Neuromorphic architecture has been suggested as an alternative to the existing von Neumann counterpart. The neuromorphic approach allows massively parallel processing and asynchronous timing schemes with low power consumption. This work presents ALD- NbOx thin films as a potential material for neuromorphic computation. NbO2 shows metal-insulator transition which is the desired property for threshold switching (TS). However, direct forming of NbO2 is rather difficult and deposited NbOx tends predominantly to result in Nb2O5. To obtain NbO2 we used an oxygen scavenger layer of Ti to alter Nb2O5

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Marquez, Bicky A., Hugh Morison, Zhimu Guo, Matthew Filipovich, Paul R. Prucnal, and Bhavin J. Shastri. "Graphene-based photonic synapse for multi wavelength neural networks." MRS Advances 5, no. 37-38 (2020): 1909–17. http://dx.doi.org/10.1557/adv.2020.327.

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AbstractA synapse is a junction between two biological neurons, and the strength, or weight of the synapse, determines the communication strength between the neurons. Building a neuromorphic (i.e. neuron isomorphic) computing architecture, inspired by a biological network or brain, requires many engineered synapses. Furthermore, recent investigation in neuromorphic photonics, i.e. neuromorphic architectures on photonics platforms, have garnered much interest to enable high-bandwidth, low-latency, low-energy applications of neural networks in machine learning and neuromorphic computing. We prop

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Olin-Ammentorp, Wilkie, and Nathaniel Cady. "Biologically-Inspired Neuromorphic Computing." Science Progress 102, no. 3 (2019): 261–76. http://dx.doi.org/10.1177/0036850419850394.

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Advances in integrated circuitry from the 1950s to the present day have enabled a revolution in technology across the world. However, fundamental limits of circuitry make further improvements through historically successful methods increasingly challenging. It is becoming clear that to address new challenges and applications, new methods of computation will be required. One promising field is neuromorphic engineering, a broad field which applies biologically inspired principles to create alternative computational architectures and methods. We address why neuromorphic engineering is one of the

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Kim, Dongshin, Ik-Jyae Kim, and Jang-Sik Lee. "Memory Devices for Flexible and Neuromorphic Device Applications." Advanced Intelligent Systems 3, no. 5 (2021): 2000206. http://dx.doi.org/10.1002/aisy.202000206.

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Polian, Ilia, John P. Hayes, Vincent T. Lee, and Weikang Qian. "Guest Editors’ Introduction: Stochastic Computing for Neuromorphic Applications." IEEE Design & Test 38, no. 6 (2021): 5–15. http://dx.doi.org/10.1109/mdat.2021.3080989.

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Shen Liu-feng, Hu Ling-xiang, Kang Feng-wen, Ye Yu-min, and Zhuge Fei. "Optoelectronic neuromorphic devices and their applications." Acta Physica Sinica, 2022, 0. http://dx.doi.org/10.7498/aps.71.20220111.

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Conventional computers based on the von Neumann architecture are inefficient in parallel computing and self-adaptive learning, and therefore cannot meet the rapid development of information technology that needs efficient and high-speed computing. Due to the unique advantages such as high parallelism and ultralow power consumption, bioinspired neuromorphic computing can have the capability to break through the bottlenecks of conventional computers and is now considered as an ideal choice to realize the next-generation artificial intelligence. As the hardware carriers that allow implementing ne

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Aboumerhi, Khaled, Amparo Güemes, Hongtao Liu, Francesco V. Tenore, and Ralph Etienne-Cummings. "Neuromorphic applications in medicine." Journal of Neural Engineering, August 2, 2023. http://dx.doi.org/10.1088/1741-2552/aceca3.

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Abstract In recent years, there has been a growing demand for miniaturization, low power consumption, quick treatments, and non-invasive clinical strategies in the healthcare industry. To meet these demands, healthcare professionals are seeking new technological paradigms that can improve diagnostic accuracy while ensuring patient compliance. Neuromorphic engineering, which uses neural models in hardware and software to replicate brain-like behaviors, can help usher in a new era of medicine by delivering low power, low latency, small footprint, and high bandwidth solutions. This paper provides

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Kulshrestha, Sanatan. "Neuromorphic Chips Defence Applications." SSRN Electronic Journal, 2016. http://dx.doi.org/10.2139/ssrn.2773015.

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Huang, Zhuohui, Yanran Li, Yi Zhang, Jiewei Chen, Jun He, and Jie Jiang. "2D Multifunctional Devices: from Material Preparation to Device Fabrication and Neuromorphic Applications." International Journal of Extreme Manufacturing, February 28, 2024. http://dx.doi.org/10.1088/2631-7990/ad2e13.

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Abstract Neuromorphic computing systems, which mimic the operation of neurons and synapses in the human brain, are seen as an appealing next-generation computing method due to their strong and efficient computing abilities. Two-dimensional (2D) materials with dangling bond-free surfaces and atomic-level thicknesses have emerged as promising candidates for neuromorphic computing hardware. As a result, 2D neuromorphic devices may provide an ideal platform for developing multifunctional neuromorphic applications. Here, we review the recent neuromorphic devices based on 2D material and their multi

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Enuganti, Pavan Kumar, Basabdatta Sen Bhattacharya, Teresa Serrano Gotarredona, and Oliver Rhodes. "Neuromorphic Computing and Applications: A Topical Review." WIREs Data Mining and Knowledge Discovery 15, no. 2 (2025). https://doi.org/10.1002/widm.70014.

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ABSTRACTNeuromorphic computers achieve energy efficiency by emulating brain structure and event‐driven processing that reduces energy consumption significantly. An increasing interest in this technology started in the initial years of this millennium, sparked by the awareness and concern on the ever‐increasing power demands of modern‐day computing. In current times, there are several neuromorphic computers and sensors that continue to be developed in both industry and academic research. The focus of this survey is on the neuromorphic computing applications of these devices that include brain‐i

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Patil, Chandrashekhar S., Sourabh B. Ghode, Jungmin Kim, et al. "Neuromorphic devices for electronic skin applications." Materials Horizons, 2025. https://doi.org/10.1039/d4mh01848f.

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Neuromorphic devices represent an important advancement in technology, drawing inspiration from the intricate and efficient mechanisms of the human brain. This review paper elucidates the diverse landscape of neuromorphic electronic...

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Lin, Xiangde, Zhenyu Feng, Yao Xiong, et al. "Piezotronic Neuromorphic Devices: Principle, Manufacture, and Applications." International Journal of Extreme Manufacturing, March 13, 2024. http://dx.doi.org/10.1088/2631-7990/ad339b.

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Abstract With the arrival of the era of artificial intelligence (AI) and big data, the explosive growth of data has raised higher demands on computer hardware and systems. Neuromorphic techniques inspired by biological nervous systems are expected to be one of the approaches to break the von Neumann bottleneck. Piezotronic neuromorphic devices modulate electrical transport characteristics by piezopotential and directly associate external mechanical motion with electrical output signals in an active manner, with the capability to sense/store/process information of external stimuli. In this revi

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