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Journal articles on the topic 'Neuromorphic applications'

Author: Grafiati

Published: 6 September 2023

Last updated: 7 July 2024

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1

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

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This paper introduces a multidisciplinary conceptual perspective encompassing artificial intelligence (AI), artificial general intelligence (AGI), and cybernetics, framed within what we call the formalism of generalized neuromorphism. Drawing from recent advancements in computing, such as neuromorphic computing and spiking neural networks, as well as principles from the theory of open dynamical systems and stochastic classical and quantum dynamics, this formalism is tailored to model generic networks comprising abstract processing events. A pivotal aspect of our approach is the incorporation of the memory space and the intrinsic non-Markovian nature of the abstract generalized neuromorphic system. We envision future computations taking place within an expanded space (memory space) and leveraging memory states. Positioned at a high abstract level, generalized neuromorphism facilitates multidisciplinary applications across various approaches within the AI community.

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2

Park, Jisoo, Jihyun Shin, and Hocheon Yoo. "Heterostructure-Based Optoelectronic Neuromorphic Devices." Electronics 13, no. 6 (March 14, 2024): 1076. http://dx.doi.org/10.3390/electronics13061076.

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The concept of neuromorphic devices, aiming to process large amounts of information in parallel, at low power, high speed, and high efficiency, is to mimic the functions of human brain by emulating biological neural behavior. Optoelectronic neuromorphic devices are particularly suitable for neuromorphic applications with their ability to generate various pulses based on wavelength and to control synaptic stimulation. Each wavelength (ultraviolet, visible, and infrared) has specific advantages and optimal applications. Here, the heterostructure-based optoelectronic neuromorphic devices are explored across the full wavelength range (ultraviolet to infrared) by categorizing them on the basis of irradiated wavelength and structure (two-terminal and three-terminal) with respect to emerging optoelectrical materials. The relationship between neuromorphic applications, light wavelength, and mechanism is revisited. Finally, the potential and challenging aspects of next-generation optoelectronic neuromorphic devices are presented, which can assist in the design of suitable materials and structures for neuromorphic-based applications.

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3

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

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4

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

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As technologies like the Internet, artificial intelligence, and big data evolve at a rapid pace, computer architecture is transitioning from compute-intensive to memory-intensive. However, traditional von Neumann architectures encounter bottlenecks in addressing modern computational challenges. The emulation of the behaviors of a synapse at the device level by ionic/electronic devices has shown promising potential in future neural-inspired and compact artificial intelligence systems. To address these issues, this review thoroughly investigates the recent progress in metal-oxide heterostructures for neuromorphic applications. These heterostructures not only offer low power consumption and high stability but also possess optimized electrical characteristics via interface engineering. The paper first outlines various synthesis methods for metal oxides and then summarizes the neuromorphic devices using these materials and their heterostructures. More importantly, we review the emerging multifunctional applications, including neuromorphic vision, touch, and pain systems. Finally, we summarize the future prospects of neuromorphic devices with metal-oxide heterostructures and list the current challenges while offering potential solutions. This review provides insights into the design and construction of metal-oxide devices and their applications for neuromorphic systems.

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5

Schuman, Catherine, Robert Patton, Shruti Kulkarni, Maryam Parsa, Christopher Stahl, N. Quentin Haas, J. Parker Mitchell, et al. "Evolutionary vs imitation learning for neuromorphic control at the edge*." Neuromorphic Computing and Engineering 2, no. 1 (January 24, 2022): 014002. http://dx.doi.org/10.1088/2634-4386/ac45e7.

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Abstract Neuromorphic computing offers the opportunity to implement extremely low power artificial intelligence at the edge. Control applications, such as autonomous vehicles and robotics, are also of great interest for neuromorphic systems at the edge. It is not clear, however, what the best neuromorphic training approaches are for control applications at the edge. In this work, we implement and compare the performance of evolutionary optimization and imitation learning approaches on an autonomous race car control task using an edge neuromorphic implementation. We show that the evolutionary approaches tend to achieve better performing smaller network sizes that are well-suited to edge deployment, but they also take significantly longer to train. We also describe a workflow to allow for future algorithmic comparisons for neuromorphic hardware on control applications at the edge.

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6

Kurshan, Eren, Hai Li, Mingoo Seok, and Yuan Xie. "A Case for 3D Integrated System Design for Neuromorphic Computing and AI Applications." International Journal of Semantic Computing 14, no. 04 (December 2020): 457–75. http://dx.doi.org/10.1142/s1793351x20500063.

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Over the last decade, artificial intelligence (AI) has found many applications areas in the society. As AI solutions have become more sophistication and the use cases grew, they highlighted the need to address performance and energy efficiency challenges faced during the implementation process. To address these challenges, there has been growing interest in neuromorphic chips. Neuromorphic computing relies on non von Neumann architectures as well as novel devices, circuits and manufacturing technologies to mimic the human brain. Among such technologies, three-dimensional (3D) integration is an important enabler for AI hardware and the continuation of the scaling laws. In this paper, we overview the unique opportunities 3D integration provides in neuromorphic chip design, discuss the emerging opportunities in next generation neuromorphic architectures and review the obstacles. Neuromorphic architectures, which relied on the brain for inspiration and emulation purposes, face grand challenges due to the limited understanding of the functionality and the architecture of the human brain. Yet, high-levels of investments are dedicated to develop neuromorphic chips. We argue that 3D integration not only provides strategic advantages to the cost-effective and flexible design of neuromorphic chips, it may provide design flexibility in incorporating advanced capabilities to further benefit the designs in the future.

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7

Huang, Heyi, Chen Ge, Zhuohui Liu, Hai Zhong, Erjia Guo, Meng He, Can Wang, Guozhen Yang, and Kuijuan Jin. "Electrolyte-gated transistors for neuromorphic applications." Journal of Semiconductors 42, no. 1 (January 1, 2021): 013103. http://dx.doi.org/10.1088/1674-4926/42/1/013103.

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8

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

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9

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

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Bio-inspired neuromorphic computing has aroused great interest due to its potential to realize on-chip learning with bio-plausibility and energy efficiency. Realizing spike-timing-dependent plasticity (STDP) in synaptic electronics is critical toward bio-inspired neuromorphic computing systems. Here, we report on stochastic artificial synapses based on nanoscale magnetic tunnel junctions that can implement STDP harnessing stochastic magnetization switching. We further demonstrate that both the magnitude and the temporal requirements for STDP can be modulated via engineering the pre- and post-synaptic voltage pulses. Moreover, based on arrays of binary magnetic synapses, unsupervised learning can be realized for neuromorphic computing tasks such as pattern recognition with great computing accuracy and efficiency. Our study suggests a potential route toward on-chip neuromorphic computing systems.

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10

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

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11

Marquez, Bicky A., Matthew J. Filipovich, Emma R. Howard, Viraj Bangari, Zhimu Guo, Hugh D. Morison, Thomas Ferreira De Lima, Alexander N. Tait, Paul R. Prucnal, and Bhavin J. Shastri. "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 learning. We discuss current progress and challenges on these demonstrations to scale to practical systems for training and inference.

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12

Tyler, Neil. "Tempo Targets Low-Power Chips for AI Applications." New Electronics 52, no. 13 (July 9, 2019): 7. http://dx.doi.org/10.12968/s0047-9624(22)61557-8.

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13

Huang, Yi, Fatemeh Kiani, Fan Ye, and Qiangfei Xia. "From memristive devices to neuromorphic systems." Applied Physics Letters 122, no. 11 (March 13, 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 circuit designs have been proposed for neuromorphic computing and applied to different proof-of-concept applications, there is still a long way to go to build large-scale low-power memristor-based neuromorphic systems that can bridge the gap between AI and biological brains. This Perspective summarizes the progress and challenges from memristor devices to neuromorphic systems and proposes possible directions for neuromorphic system implementation based on memristive devices.

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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 (December 12, 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 for neuromorphic applications.

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15

Xu, Jiaqi, Xiaoning Zhao, Xiaoli Zhao, Zhongqiang Wang, Qingxin Tang, Haiyang Xu, and Yichun Liu. "Memristors with Biomaterials for Biorealistic Neuromorphic Applications." Small Science 2, no. 10 (October 2022): 2270020. http://dx.doi.org/10.1002/smsc.202270020.

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16

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 (January 2022): 10–19. http://dx.doi.org/10.1038/s43588-021-00184-y.

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17

Hajtó, Dániel, Ádám Rák, and György Cserey. "Robust Memristor Networks for Neuromorphic Computation Applications." Materials 12, no. 21 (October 31, 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 measurements, by gaining similar results as in previous simulations. These results can lead to more stable devices, which are a necessity for neuromorphic computation, artificial intelligence and neural network applications.

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18

Erokhin, Victor. "Memristive Devices for Neuromorphic Applications: Comparative Analysis." BioNanoScience 10, no. 4 (October 8, 2020): 834–47. http://dx.doi.org/10.1007/s12668-020-00795-1.

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19

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 exploitation of material defects. Applications in artificial intelligence, edge computing, sensor networks, and robotics are highlighted, showcasing the potential of 2D materials to revolutionize these fields. The paper also addresses the challenges related to scalability, uniformity, and energy efficiency, and concludes by offering perspectives on future research directions in this burgeoning field. This comprehensive study underscores the significance of 2D materials in advancing neuromorphic computing, paving the way for more efficient, powerful, and brain-like artificial intelligence systems.

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20

Guo, Zhonghao. "Synaptic device-based neuromorphic computing in artificial intelligence." Applied and Computational Engineering 65, no. 1 (May 23, 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 devices, as well as the basic framework and algorithms of neuromorphic computing. Then, the paper analyzes the advantages and difficulties of neuromorphic computing based on synaptic devices, including the preparation, testing, modelling and integration of the devices, as well as the systems architecture, programming and optimization. Then, this paper gives examples of the applications and effects of synaptic device-based neuromorphic computing in artificial intelligence fields such as image processing and natural language processing, including image denoising, image segmentation, image recognition, text classification, text summarization, and text generation. Finally, this paper summarizes the current research status and future synaptic device-based neuromorphic computing trends. It puts forward some research directions and suggestions to promote the development and innovation in this field.

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21

Martins, Raquel Azevedo, Emanuel Carlos, Jonas Deuermeier, Maria Elias Pereira, Rodrigo Martins, Elvira Fortunato, and Asal Kiazadeh. "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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22

Blachowicz, Tomasz, and Andrea Ehrmann. "Magnetic Elements for Neuromorphic Computing." Molecules 25, no. 11 (May 30, 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 using magnetic elements, published during the last years. Magnetic elements play an important role in neuromorphic computing. While other approaches, such as optical and conductive elements, are also under investigation in many groups, magnetic nanostructures and generally magnetic materials offer large advantages, especially in terms of data storage, but they can also unambiguously be used for data transport, e.g., by propagation of skyrmions or domain walls. This review underlines the possible applications of magnetic materials and nanostructures in neuromorphic systems.

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23

Elitalib, Elmunazir Husein, and Asnidar A. Ani Bahar. "Neuromorphic Computing Architectures for Real-time Image Processing and Pattern Recognition." Algorithm Asynchronous 1, no. 1 (August 29, 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 neural networks and are consistent with earlier studies. Comparisons with hybrid architectures highlight the Neuromorphic architecture's strength as a stand-alone system and point to simpler implementations. However, issues with accuracy fluctuation and the requirement for scalability continue, emphasizing areas for more study. The energy economy of neuromorphic architectures makes them essential parts of real-time image processing and pattern recognition as the field develops.

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Moon, Jaehyun, Ju-Hun Lee, Kitae Kim, Junho Kim, Soohyung Park, Yeonjin Yi, and Seung-Youl Kang. "Threshold Switching of ALD-NbO_x Films for Neuromorphic Applications." ECS Meeting Abstracts MA2023-02, no. 30 (December 22, 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 to NbO2. After a proper electroforming process, the device with a Ti insert showed metal-insulator transition above a specific voltage, i.e., a threshold voltage, and a stable threshold switching characteristics with wide operation range, characteristics not observed in the absence of a Ti insert. The well-defined TS characteristics clearly indicated the Ti role in controlling the oxidation state of NbOx.To verify the oxidation state of NbOx, XPS depth profile analyses were used. The presence of a Ti insertion layer resulted in an increase of 10~30% in Nb4+ states, depending on the total thickness of the NbOx films, while Nb5+ states decreased. Bearing in mind practical applications, we used only CMOS-compatible materials and processes to fabricate TS devices. Our approach suggests a reliable method to fabricate NbO2 neurons. Acknowledgment: This work was supported by Electronics and Telecommunications Research Institute (ETRI) grant funded by the Korean government under grant " Non-CMOS Neuromorphic Device Basic Technology." (22BB1110).

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Feng, Chenyin, Wenwei Wu, Huidi Liu, Junke Wang, Houzhao Wan, Guokun Ma, and Hao Wang. "Emerging Opportunities for 2D Materials in Neuromorphic Computing." Nanomaterials 13, no. 19 (October 7, 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, including graphene, transition metal dichalcogenides (TMDs), hexagonal boron nitride (h-BN), and black phosphorus (BP), for neuromorphic computing applications. The potential of these materials in neuromorphic computing is discussed from the perspectives of material properties, growth methods, and device operation principles.

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Olin-Ammentorp, Wilkie, and Nathaniel Cady. "Biologically-Inspired Neuromorphic Computing." Science Progress 102, no. 3 (May 14, 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 most promising fields within emerging computational technology, elaborating on its common principles and models, and discussing its current state and future challenges.

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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 propose a graphene-based synapse model as a core element to enable large-scale photonic neural networks based on on-chip multiwavelength techniques. This device consists of an electro-absorption modulator embedded in a microring resonator. We also introduce an encoding protocol that allows for the representation of synaptic weights on our photonic device with 15.7 bits of resolution using current control hardware. Recent work has suggested that graphene-based modulators could operate in excess of 100 GHz. Combined with our work, such a graphene-based synapse could enable applications for ultrafast and online learning.

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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 (January 25, 2021): 2000206. http://dx.doi.org/10.1002/aisy.202000206.

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29

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 (December 2021): 5–15. http://dx.doi.org/10.1109/mdat.2021.3080989.

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Miranda, Enrique, and Jordi Suñé. "Memristors for Neuromorphic Circuits and Artificial Intelligence Applications." Materials 13, no. 4 (February 20, 2020): 938. http://dx.doi.org/10.3390/ma13040938.

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Artificial Intelligence has found many applications in the last decade due to increased computing power. Artificial Neural Networks are inspired in the brain structure and consist in the interconnection of artificial neurons through artificial synapses in the so-called Deep Neural Networks (DNNs). Training these systems requires huge amounts of data and, after the network is trained, it can recognize unforeseen data and provide useful information. As far as the training is concerned, we can distinguish between supervised and unsupervised learning. The former requires labelled data and is based on the iterative minimization of the output error using the stochastic gradient descent method followed by the recalculation of the strength of the synaptic connections (weights) with the backpropagation algorithm. On the other hand, unsupervised learning does not require data labeling and it is not based on explicit output error minimization. Conventional ANNs can function with supervised learning algorithms (perceptrons, multi-layer perceptrons, convolutional networks, etc.) but also with unsupervised learning rules (Kohonen networks, self-organizing maps, etc.). Besides, another type of neural networks are the so-called Spiking Neural Networks (SNNs) in which learning takes place through the superposition of voltage spikes launched by the neurons. Their behavior is much closer to the brain functioning mechanisms they can be used with supervised and unsupervised learning rules. Since learning and inference is based on short voltage spikes, energy efficiency improves substantially. Up to this moment, all these ANNs (spiking and conventional) have been implemented as software tools running on conventional computing units based on the von Neumann architecture. However, this approach reaches important limits due to the required computing power, physical size and energy consumption. This is particularly true for applications at the edge of the internet. Thus, there is an increasing interest in developing AI tools directly implemented in hardware for this type of applications. The first hardware demonstrations have been based on Complementary Metal-Oxide-Semiconductor (CMOS) circuits and specific communication protocols. However, to further increase training speed andenergy efficiency while reducing the system size, the combination of CMOS neuron circuits with memristor synapses is now being explored. It has also been pointed out that the short time non-volatility of some memristors may even allow fabricating purely memristive ANNs. The memristor is a new device (first demonstrated in solid-state in 2008) which behaves as a resistor with memory and which has been shown to have potentiation and depression properties similar to those of biological synapses. In this Special Issue, we explore the state of the art of neuromorphic circuits implementing neural networks with memristors for AI applications.

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Chen, Yu, Gang Liu, Cheng Wang, Wenbin Zhang, Run-Wei Li, and Luxing Wang. "Polymer memristor for information storage and neuromorphic applications." Materials Horizons 1, no. 5 (June 2, 2014): 489. http://dx.doi.org/10.1039/c4mh00067f.

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Luo, Zheng-Dong, Ming-Min Yang, and Marin Alexe. "Dissolvable Memristors for Physically Transient Neuromorphic Computing Applications." ACS Applied Electronic Materials 2, no. 2 (December 13, 2019): 310–15. http://dx.doi.org/10.1021/acsaelm.9b00670.

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Oh, Chadol, and Junwoo Son. "Hydrogen Sensor and Neuromorphic Applications Using Correlated Materials." Ceramist 22, no. 1 (March 31, 2019): 17–26. http://dx.doi.org/10.31613/ceramist.2019.22.1.02.

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Gaba, Siddharth, Patrick Sheridan, Jiantao Zhou, Shinhyun Choi, and Wei Lu. "Stochastic memristive devices for computing and neuromorphic applications." Nanoscale 5, no. 13 (2013): 5872. http://dx.doi.org/10.1039/c3nr01176c.

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35

Yu, Fei, and Li Qiang Zhu. "Ionotronic Neuromorphic Devices for Bionic Neural Network Applications." physica status solidi (RRL) – Rapid Research Letters 13, no. 6 (June 2019): 1970022. http://dx.doi.org/10.1002/pssr.201970025.

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Tian, He, Qiushi Guo, Yujun Xie, Huan Zhao, Cheng Li, Judy J. Cha, Fengnian Xia, and Han Wang. "Anisotropic Black Phosphorus Synaptic Device for Neuromorphic Applications." Advanced Materials 28, no. 25 (April 27, 2016): 4991–97. http://dx.doi.org/10.1002/adma.201600166.

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Gerasimov, Jennifer Y., Roger Gabrielsson, Robert Forchheimer, Eleni Stavrinidou, Daniel T. Simon, Magnus Berggren, and Simone Fabiano. "An Evolvable Organic Electrochemical Transistor for Neuromorphic Applications." Advanced Science 6, no. 7 (February 4, 2019): 1801339. http://dx.doi.org/10.1002/advs.201801339.

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Wang, Chen‐Yu, Cong Wang, Fanhao Meng, Pengfei Wang, Shuang Wang, Shi‐Jun Liang, and Feng Miao. "2D Layered Materials for Memristive and Neuromorphic Applications." Advanced Electronic Materials 6, no. 2 (December 11, 2019): 1901107. http://dx.doi.org/10.1002/aelm.201901107.

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You, Tao, Miao Zhao, Zhikang Fan, and Chenwei Ju. "Emerging Memtransistors for Neuromorphic System Applications: A Review." Sensors 23, no. 12 (June 7, 2023): 5413. http://dx.doi.org/10.3390/s23125413.

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The von Neumann architecture with separate memory and processing presents a serious challenge in terms of device integration, power consumption, and real-time information processing. Inspired by the human brain that has highly parallel computing and adaptive learning capabilities, memtransistors are proposed to be developed in order to meet the requirement of artificial intelligence, which can continuously sense the objects, store and process the complex signal, and demonstrate an “all-in-one” low power array. The channel materials of memtransistors include a range of materials, such as two-dimensional (2D) materials, graphene, black phosphorus (BP), carbon nanotubes (CNT), and indium gallium zinc oxide (IGZO). Ferroelectric materials such as P(VDF-TrFE), chalcogenide (PZT), HfxZr1−xO2(HZO), In2Se3, and the electrolyte ion are used as the gate dielectric to mediate artificial synapses. In this review, emergent technology using memtransistors with different materials, diverse device fabrications to improve the integrated storage, and the calculation performance are demonstrated. The different neuromorphic behaviors and the corresponding mechanisms in various materials including organic materials and semiconductor materials are analyzed. Finally, the current challenges and future perspectives for the development of memtransistors in neuromorphic system applications are presented.

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Kutluyarov, Ruslan V., Aida G. Zakoyan, Grigory S. Voronkov, Elizaveta P. Grakhova, and Muhammad A. Butt. "Neuromorphic Photonics Circuits: Contemporary Review." Nanomaterials 13, no. 24 (December 14, 2023): 3139. http://dx.doi.org/10.3390/nano13243139.

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Neuromorphic photonics is a cutting-edge fusion of neuroscience-inspired computing and photonics technology to overcome the constraints of conventional computing architectures. Its significance lies in the potential to transform information processing by mimicking the parallelism and efficiency of the human brain. Using optics and photonics principles, neuromorphic devices can execute intricate computations swiftly and with impressive energy efficiency. This innovation holds promise for advancing artificial intelligence and machine learning while addressing the limitations of traditional silicon-based computing. Neuromorphic photonics could herald a new era of computing that is more potent and draws inspiration from cognitive processes, leading to advancements in robotics, pattern recognition, and advanced data processing. This paper reviews the recent developments in neuromorphic photonic integrated circuits, applications, and current challenges.

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Bhat, Pranava. "Analysis of Neuromorphic Computing Systems and its Applications in Machine Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 5309–12. http://dx.doi.org/10.22214/ijraset.2021.35601.

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The domain of engineering has always taken inspiration from the biological world. Understanding the functionalities of the human brain is one of the key areas of interest over time and has caused many advancements in the field of computing systems. The computational capability per unit power per unit volume of the human brain exceeds the current best supercomputers. Mimicking the physics of computations used by the nervous system and the brain can bring a paradigm shift to the computing systems. The concept of bridging computing and neural systems can be termed as neuromorphic computing and it is bringing revolutionary changes in the computing hardware. Neuromorphic computing systems have seen swift progress in the past decades. Many organizations have introduced a variety of designs, implementation methodologies and prototype chips. This paper discusses the parameters that are considered in the advanced neuromorphic computing systems and the tradeoffs between them. There have been attempts made to make computer models of neurons. Advancements in the hardware implementation are fuelling the applications in the field of machine learning. This paper presents the applications of these modern computing systems in Machine Learning.

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Niu, Xuezhong, Bobo Tian, Qiuxiang Zhu, Brahim Dkhil, and Chungang Duan. "Ferroelectric polymers for neuromorphic computing." Applied Physics Reviews 9, no. 2 (June 2022): 021309. http://dx.doi.org/10.1063/5.0073085.

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The last few decades have witnessed the rapid development of electronic computers relying on von Neumann architecture. However, due to the spatial separation of the memory unit from the computing processor, continuous data movements between them result in intensive time and energy consumptions, which unfortunately hinder the further development of modern computers. Inspired by biological brain, the in situ computing of memristor architectures, which has long been considered to hold unprecedented potential to solve the von Neumann bottleneck, provides an alternative network paradigm for the next-generation electronics. Among the materials for designing memristors, i.e., nonvolatile memories with multistate tunable resistances, ferroelectric polymers have drawn much research interest due to intrinsic analog switching property and excellent flexibility. In this review, recent advances on artificial synapses based on solution-processed ferroelectric polymers are discussed. The relationship between materials' properties, structural design, switching mechanisms, and systematic applications is revealed. We first introduce the commonly used ferroelectric polymers. Afterward, device structures and the switching mechanisms underlying ferroelectric synapse are discussed. The current applications of organic ferroelectric synapses in advanced neuromorphic systems are also summarized. Eventually, the remaining challenges and some strategies to eliminate non-ideality of synaptic devices are analyzed.

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Qian, Fangsheng, Xiaobo Bu, Junjie Wang, Ziyu Lv, Su-Ting Han, and Ye Zhou. "Evolutionary 2D organic crystals for optoelectronic transistors and neuromorphic computing." Neuromorphic Computing and Engineering 2, no. 1 (February 7, 2022): 012001. http://dx.doi.org/10.1088/2634-4386/ac4a84.

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Abstract Brain-inspired neuromorphic computing has been extensively researched, taking advantage of increased computer power, the acquisition of massive data, and algorithm optimization. Neuromorphic computing requires mimicking synaptic plasticity and enables near-in-sensor computing. In synaptic transistors, how to elaborate and examine the link between microstructure and characteristics is a major difficulty. Due to the absence of interlayer shielding effects, defect-free interfaces, and wide spectrum responses, reducing the thickness of organic crystals to the 2D limit has a lot of application possibilities in this computing paradigm. This paper presents an update on the progress of 2D organic crystal-based transistors for data storage and neuromorphic computing. The promises and synthesis methodologies of 2D organic crystals (2D OCs) are summarized. Following that, applications of 2D OCs for ferroelectric non-volatile memory, circuit-type optoelectronic synapses, and neuromorphic computing are addressed. Finally, new insights and challenges for the field’s future prospects are presented, pushing the boundaries of neuromorphic computing even farther.

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Waser, Rainer, Regina Dittmann, Stephan Menzel, and Tobias Noll. "Introduction to new memory paradigms: memristive phenomena and neuromorphic applications." Faraday Discussions 213 (2019): 11–27. http://dx.doi.org/10.1039/c8fd90058b.

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Yan, Yujie, Xiaomin Wu, Qizhen Chen, Xiumei Wang, Enlong Li, Yuan Liu, Huipeng Chen, and Tailiang Guo. "An intrinsically healing artificial neuromorphic device." Journal of Materials Chemistry C 8, no. 20 (2020): 6869–76. http://dx.doi.org/10.1039/d0tc00726a.

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Clair, Judicael, Guy Eichler, and Luca P. Carloni. "SpikeHard: Efficiency-Driven Neuromorphic Hardware for Heterogeneous Systems-on-Chip." ACM Transactions on Embedded Computing Systems 22, no. 5s (September 9, 2023): 1–22. http://dx.doi.org/10.1145/3609101.

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Neuromorphic computing is an emerging field with the potential to offer performance and energy-efficiency gains over traditional machine learning approaches. Most neuromorphic hardware, however, has been designed with limited concerns to the problem of integrating it with other components in a heterogeneous System-on-Chip (SoC). Building on a state-of-the-art reconfigurable neuromorphic architecture, we present the design of a neuromorphic hardware accelerator equipped with a programmable interface that simplifies both the integration into an SoC and communication with the processor present on the SoC. To optimize the allocation of on-chip resources, we develop an optimizer to restructure existing neuromorphic models for a given hardware architecture, and perform design-space exploration to find highly efficient implementations. We conduct experiments with various FPGA-based prototypes of many-accelerator SoCs, where Linux-based applications running on a RISC-V processor invoke Pareto-optimal implementations of our accelerator alongside third-party accelerators. These experiments demonstrate that our neuromorphic hardware, which is up to 89× faster and 170× more energy efficient after applying our optimizer, can be used in synergy with other accelerators for different application purposes.

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Chen, Lin, Tian-Yu Wang, Ya-Wei Dai, Ming-Yang Cha, Hao Zhu, Qing-Qing Sun, Shi-Jin Ding, Peng Zhou, Leon Chua, and David Wei Zhang. "Ultra-low power Hf0.5Zr0.5O2 based ferroelectric tunnel junction synapses for hardware neural network applications." Nanoscale 10, no. 33 (2018): 15826–33. http://dx.doi.org/10.1039/c8nr04734k.

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Jeon, Yunchae, Donghyun Lee, and Hocheon Yoo. "Recent Advances in Metal-Oxide Thin-Film Transistors: Flexible/Stretchable Devices, Integrated Circuits, Biosensors, and Neuromorphic Applications." Coatings 12, no. 2 (February 4, 2022): 204. http://dx.doi.org/10.3390/coatings12020204.

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Thin-film transistors using metal oxides have been investigated extensively because of their high transparency, large area, and mass production of metal oxide semiconductors. Compatibility with conventional semiconductor processes, such as photolithography of the metal oxide offers the possibility to develop integrated circuits on a larger scale. In addition, combinations with other materials have enabled the development of sensor applications or neuromorphic devices in recent years. Here, this paper provides a timely overview of metal-oxide-based thin-film transistors focusing on emerging applications, including flexible/stretchable devices, integrated circuits, biosensors, and neuromorphic devices. This overview also revisits recent efforts on metal oxide-based thin-film transistors developed with high compatibility for integration to newly reported applications.

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Jaafar, Ayoub H., Robert J. Gray, Emanuele Verrelli, Mary O'Neill, Stephen M. Kelly, and Neil T. Kemp. "Reversible optical switching memristors with tunable STDP synaptic plasticity: a route to hierarchical control in artificial intelligent systems." Nanoscale 9, no. 43 (2017): 17091–98. http://dx.doi.org/10.1039/c7nr06138b.

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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 neuromorphic computing, neuromorphic devices are very critical in building neuromorphic chips. Meanwhile, the development of human visual systems and optogenetics also provide new insights into how to carry out the research of neuromorphic devices. The emerging optoelectronic neuromorphic devices feature the unique advantages of photonics and electronics, showing great potential in the neuromorphic computing field and attracting more and more attention of the scientists at at home and abroad. In view of these, the main purpose of this review is to disclose the recent advances of optoelectronic neuromorphic devices and the prospects of their practical applications. We first review the artificial optoelectronic synapses and neurons, including device structural features, working mechanism principles, neuromorphic simulation functions, and so on. Then, we exhibit and introduce the applications of optoelectronic neuromorphic devices particularly suitable for the fields involving artificial vision systems, artificial perception systems, neuromorphic computing etc. At last, we summarize the challenges the optoelectronic neuromorphic devices are currently facing, and disclose some perspectives about their development directions in the future.

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