Journal articles: 'Neuromorphic technologies'

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

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Argyris, Apostolos. "Photonic neuromorphic technologies in optical communications." Nanophotonics 11, no. 5 (January 19, 2022): 897–916. http://dx.doi.org/10.1515/nanoph-2021-0578.

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Abstract Machine learning (ML) and neuromorphic computing have been enforcing problem-solving in many applications. Such approaches found fertile ground in optical communications, a technological field that is very demanding in terms of computational speed and complexity. The latest breakthroughs are strongly supported by advanced signal processing, implemented in the digital domain. Algorithms of different levels of complexity aim at improving data recovery, expanding the reach of transmission, validating the integrity of the optical network operation, and monitoring data transfer faults. Lately, the concept of reservoir computing (RC) inspired hardware implementations in photonics that may offer revolutionary solutions in this field. In a brief introduction, I discuss some of the established digital signal processing (DSP) techniques and some new approaches based on ML and neural network (NN) architectures. In the main part, I review the latest neuromorphic computing proposals that specifically apply to photonic hardware and give new perspectives on addressing signal processing in optical communications. I discuss the fundamental topologies in photonic feed-forward and recurrent network implementations. Finally, I review the photonic topologies that were initially tested for channel equalization benchmark tasks, and then in fiber transmission systems, for optical header recognition, data recovery, and modulation format identification.

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Kim, Chul-Heung, Suhwan Lim, Sung Yun Woo, Won-Mook Kang, Young-Tak Seo, Sung-Tae Lee, Soochang Lee, et al. "Emerging memory technologies for neuromorphic computing." Nanotechnology 30, no. 3 (November 13, 2018): 032001. http://dx.doi.org/10.1088/1361-6528/aae975.

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Varshika, M. Lakshmi, Federico Corradi, and Anup Das. "Nonvolatile Memories in Spiking Neural Network Architectures: Current and Emerging Trends." Electronics 11, no. 10 (May 18, 2022): 1610. http://dx.doi.org/10.3390/electronics11101610.

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

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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” created in the 1960s, by focusing on the work of Leon Harmon and his “neuromime,” whose material history overlapped in a very interesting sense with the visual and artistic culture of the second half of the 20th century.

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

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Woo, Jiyong, Jeong Hun Kim, Jong‐Pil Im, and Seung Eon Moon. "Recent Advancements in Emerging Neuromorphic Device Technologies." Advanced Intelligent Systems 2, no. 10 (August 23, 2020): 2000111. http://dx.doi.org/10.1002/aisy.202000111.

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

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Kurshan, Eren, Hai Li, Mingoo Seok, and Yuan Xie. "A Case for 3D Integrated System Design for Neuromorphic Computing and AI Applications." International Journal of Semantic Computing 14, no. 04 (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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Orii, Yasumitsu, Akihiro Horibe, Kuniaki Sueoka, Keiji Matsumoto, Toyohiro Aoki, Hirokazu Noma, Sayuri Kohara, et al. "PERSPECTIVE ON REQUIRED PACKAGING TECHNOLOGIES FOR NEUROMORPHIC DEVICES." International Symposium on Microelectronics 2015, no. 1 (October 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. However, one million neurons only correspond to those of the bee's brain, and to mimic the brains of higher order animals, the inter-chip wiring becomes much more important, because this kind of neuromorphic device requires a large number of parallel signal lines for massive parallel signal operations. 3D chip stacking is, of course, a crucial technology in achieving the device. Technologies associated with 3D stacking such as low cost TSV formation and fine-pitch interconnection, smaller than 10μm pitch technology are required. From the reliability point of view, the optimization of solder composition is also important. Injection Molded Solder (IMS) is well fit to this fine pitch interconnection, in terms of material optimization and low cost joints. As for the interposer, the build-up organic interposer is the most attractive candidates for the cost issue, but in the most top layer, ultra-fine pitch wiring with the line and space widths smaller than 1μm should be prepared. Lots of material and process innovations are necessary for the inter-chip connection for neuromorphic devices.

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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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Pammi, Venkata Anirudh, and Sylvain Barbay. "Micro-lasers for neuromorphic computing." Photoniques, no. 104 (September 2020): 26–29. http://dx.doi.org/10.1051/photon/202010426.

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Spiking micro-lasers are interesting neuromorphic building blocks to realize all-optical spiking neural networks. Optical spike-based computing offers speed and parallelism of optical technologies combined with a sparse way of representing information in spikes, thus with a potential for efficient brain-inspired computing. This article reviews some of the latest advances in this field using single and coupled semiconductor excitable micro-lasers.

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Vanarse, Anup, Adam Osseiran, and Alexander Rassau. "Neuromorphic engineering — A paradigm shift for future IM technologies." IEEE Instrumentation & Measurement Magazine 22, no. 2 (April 2019): 4–9. http://dx.doi.org/10.1109/mim.2019.8674627.

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Schneider, Michael, Emily Toomey, Graham Rowlands, Jeff Shainline, Paul Tschirhart, and Ken Segall. "SuperMind: a survey of the potential of superconducting electronics for neuromorphic computing." Superconductor Science and Technology 35, no. 5 (March 30, 2022): 053001. http://dx.doi.org/10.1088/1361-6668/ac4cd2.

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Abstract Neuromorphic computing is a broad field that uses biological inspiration to address computing design. It is being pursued in many hardware technologies, both novel and conventional. We discuss the use of superconductive electronics for neuromorphic computing and why they are a compelling technology for the design of neuromorphic computing systems. One example is the natural spiking behavior of Josephson junctions and the ability to transmit short voltage spikes without the resistive capacitive time constants that typically hinder spike-based computing. We review the work that has been done on biologically inspired superconductive devices, circuits, and architectures and discuss the scaling potential of these demonstrations.

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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 (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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Milo, Valerio, Gerardo Malavena, Christian Monzio Compagnoni, and Daniele Ielmini. "Memristive and CMOS Devices for Neuromorphic Computing." Materials 13, no. 1 (January 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 computing functionality must be developed. This work provides an overview of the most promising device concepts in neuromorphic computing including complementary metal-oxide semiconductor (CMOS) and memristive technologies. First, the physics and operation of CMOS-based floating-gate memory devices in artificial neural networks will be addressed. Then, several memristive concepts will be reviewed and discussed for applications in deep neural network and spiking neural network architectures. Finally, the main technology challenges and perspectives of neuromorphic computing will be discussed.

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Covi, Erika, Halid Mulaosmanovic, Benjamin Max, Stefan Slesazeck, and Thomas Mikolajick. "Ferroelectric-based synapses and neurons for neuromorphic computing." Neuromorphic Computing and Engineering 2, no. 1 (February 7, 2022): 012002. http://dx.doi.org/10.1088/2634-4386/ac4918.

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Abstract The shift towards a distributed computing paradigm, where multiple systems acquire and elaborate data in real-time, leads to challenges that must be met. In particular, it is becoming increasingly essential to compute on the edge of the network, close to the sensor collecting data. The requirements of a system operating on the edge are very tight: power efficiency, low area occupation, fast response times, and on-line learning. Brain-inspired architectures such as spiking neural networks (SNNs) use artificial neurons and synapses that simultaneously perform low-latency computation and internal-state storage with very low power consumption. Still, they mainly rely on standard complementary metal-oxide-semiconductor (CMOS) technologies, making SNNs unfit to meet the aforementioned constraints. Recently, emerging technologies such as memristive devices have been investigated to flank CMOS technology and overcome edge computing systems’ power and memory constraints. In this review, we will focus on ferroelectric technology. Thanks to its CMOS-compatible fabrication process and extreme energy efficiency, ferroelectric devices are rapidly affirming themselves as one of the most promising technologies for neuromorphic computing. Therefore, we will discuss their role in emulating neural and synaptic behaviors in an area and power-efficient way.

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Chakraborty, I., A. Jaiswal, A. K. Saha, S. K. Gupta, and K. Roy. "Pathways to efficient neuromorphic computing with non-volatile memory technologies." Applied Physics Reviews 7, no. 2 (June 2020): 021308. http://dx.doi.org/10.1063/1.5113536.

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Allwood, Dan A., Matthew O. A. Ellis, David Griffin, Thomas J. Hayward, Luca Manneschi, Mohammad F. KH Musameh, Simon O'Keefe, et al. "A perspective on physical reservoir computing with nanomagnetic devices." Applied Physics Letters 122, no. 4 (January 23, 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 computing paradigm, a recurrent network with a simple training algorithm suitable for computation with spintronic devices since they can provide the properties of non-linearity and memory. We review technologies and methods for developing neuromorphic spintronic devices and conclude with critical open issues to address before such devices become widely used.

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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 (April 30, 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 in this review, including the materials and issues associated with various materials, as well as the basic switching mechanisms. Furthermore, the opportunities that CBRAMs provide for memory and brain-inspired neuromorphic computing applications, as well as the challenges that CBRAMs confront in those applications, are thoroughly discussed. The emulation of biological synapses and neurons using CBRAM devices fabricated with various switching materials and device engineering and material innovation approaches are examined in depth.

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Hao, Ji, Young-Hoon Kim, Severin N. Habisreutinger, Steven P. Harvey, Elisa M. Miller, Sean M. Foradori, Michael S. Arnold, et al. "Low-energy room-temperature optical switching in mixed-dimensionality nanoscale perovskite heterojunctions." Science Advances 7, no. 18 (April 2021): eabf1959. http://dx.doi.org/10.1126/sciadv.abf1959.

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Long-lived photon-stimulated conductance changes in solid-state materials can enable optical memory and brain-inspired neuromorphic information processing. It remains challenging to realize optical switching with low-energy consumption, and new mechanisms and design principles giving rise to persistent photoconductivity (PPC) can help overcome an important technological hurdle. Here, we demonstrate versatile heterojunctions between metal-halide perovskite nanocrystals and semiconducting single-walled carbon nanotubes that enable room-temperature, long-lived (thousands of seconds), writable, and erasable PPC. Optical switching and basic neuromorphic functions can be stimulated at low operating voltages with femto- to pico-joule energies per spiking event, and detailed analysis demonstrates that PPC in this nanoscale interface arises from field-assisted control of ion migration within the nanocrystal array. Contactless optical measurements also suggest these systems as potential candidates for photonic synapses that are stimulated and read in the optical domain. The tunability of PPC shown here holds promise for neuromorphic computing and other technologies that use optical memory.

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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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Moradi, Saber, and Rajit Manohar. "The impact of on-chip communication on memory technologies for neuromorphic systems." Journal of Physics D: Applied Physics 52, no. 1 (October 26, 2018): 014003. http://dx.doi.org/10.1088/1361-6463/aae641.

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Jha, Rashmi. "Emerging Memory Devices Beyond Conventional Data Storage: Paving the Path for Energy-Efficient Brain-Inspired Computing." Electrochemical Society Interface 32, no. 1 (March 1, 2023): 49–51. http://dx.doi.org/10.1149/2.f10231if.

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The current state of neuromorphic computing broadly encompasses domain-specific computing architectures designed to accelerate machine learning (ML) and artificial intelligence (AI) algorithms. As is well known, AI/ML algorithms are limited by memory bandwidth. Novel computing architectures are necessary to overcome this limitation. There are several options that are currently under investigation using both mature and emerging memory technologies. For example, mature memory technologies such as high-bandwidth memories (HBMs) are integrated with logic units on the same die to bring memory closer to the computing units. There are also research efforts where in-memory computing architectures have been implemented using DRAMs or flash memory technologies. However, DRAMs suffer from scaling limitations, while flash memory devices suffer from endurance issues. Additionally, in spite of this significant progress, the massive energy consumption needed in neuromorphic processors while meeting the required training and inferencing performance for AI/ML algorithms for future applications needs to be addressed. On the AI/ML algorithm side, there are several pending issues such as life-long learning, explainability, context-based decision making, multimodal association of data, adaptation to address personalized responses, and resiliency. These unresolved challenges in AI/ML have led researchers to explore brain-inspired computing architectures and paradigms.

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Abd, Hamam, and Andreas König. "On-Chip Adaptive Implementation of Neuromorphic Spiking Sensory Systems with Self-X Capabilities." Chips 2, no. 2 (June 6, 2023): 142–58. http://dx.doi.org/10.3390/chips2020009.

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In contemporary devices, the number and diversity of sensors is increasing, thus, requiring both efficient and robust interfacing to the sensors. Implementing the interfacing systems in advanced integration technologies faces numerous issues due to manufacturing deviations, signal swings, noise, etc. The interface sensor designers escape to the time domain and digital design techniques to handle these challenges. Biology gives examples of efficient machines that have vastly outperformed conventional technology. This work pursues a neuromorphic spiking sensory system design with the same efficient style as biology. Our chip, that comprises the essential elements of the adaptive neuromorphic spiking sensory system, such as the neuron, synapse, adaptive coincidence detection (ACD), and self-adaptive spike-to-rank coding (SA-SRC), was manufactured in XFAB CMOS 0.35 μm technology via EUROPRACTICE. The main emphasis of this paper is to present the measurement outcomes of the SA-SRC on-chip, evaluating the efficacy of its adaptation scheme, and assessing its capability to produce spike orders that correspond to the temporal difference between the two spikes received at its inputs. The SA-SRC plays a crucial role in performing the primary function of the adaptive neuromorphic spiking sensory system. The measurement results of the chip confirm the simulation results of our previous work.

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Concha Salor, Laura, and Victor Monzon Baeza. "Harnessing the Potential of Emerging Technologies to Break down Barriers in Tactical Communications." Telecom 4, no. 4 (October 16, 2023): 709–31. http://dx.doi.org/10.3390/telecom4040032.

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In the realm of military communications, the advent of new technologies like 5G and the future 6G networks holds promise. However, incorporating these technologies into tactical environments presents unique security challenges. This article delves into an analysis of these challenges by examining practical use cases for military communications, where emerging technologies can be applied. Our focus lies on identifying and presenting a range of emerging technologies associated with 5G and 6G, including the Internet of things (IoT), tactile internet, network virtualization and softwarization, artificial intelligence, network slicing, digital twins, neuromorphic processors, joint sensing and communications, and blockchain. We specifically explore their applicability in tactical environments by proposing where they can be potential use cases. Additionally, we provide an overview of legacy tactical radios so that they can be researched to address the challenges posed by these technologies.

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Chiappalone, Michela, Vinicius R. Cota, Marta Carè, Mattia Di Florio, Romain Beaubois, Stefano Buccelli, Federico Barban, et al. "Neuromorphic-Based Neuroprostheses for Brain Rewiring: State-of-the-Art and Perspectives in Neuroengineering." Brain Sciences 12, no. 11 (November 19, 2022): 1578. http://dx.doi.org/10.3390/brainsci12111578.

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Neuroprostheses are neuroengineering devices that have an interface with the nervous system and supplement or substitute functionality in people with disabilities. In the collective imagination, neuroprostheses are mostly used to restore sensory or motor capabilities, but in recent years, new devices directly acting at the brain level have been proposed. In order to design the next-generation of neuroprosthetic devices for brain repair, we foresee the increasing exploitation of closed-loop systems enabled with neuromorphic elements due to their intrinsic energy efficiency, their capability to perform real-time data processing, and of mimicking neurobiological computation for an improved synergy between the technological and biological counterparts. In this manuscript, after providing definitions of key concepts, we reviewed the first exploitation of a real-time hardware neuromorphic prosthesis to restore the bidirectional communication between two neuronal populations in vitro. Starting from that ‘case-study’, we provide perspectives on the technological improvements for real-time interfacing and processing of neural signals and their potential usage for novel in vitro and in vivo experimental designs. The development of innovative neuroprosthetics for translational purposes is also presented and discussed. In our understanding, the pursuit of neuromorphic-based closed-loop neuroprostheses may spur the development of novel powerful technologies, such as ‘brain-prostheses’, capable of rewiring and/or substituting the injured nervous system.

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Gao, Zhan, Yan Wang, Ziyu Lv, Pengfei Xie, Zong-Xiang Xu, Mingtao Luo, Yuqi Zhang, et al. "Ferroelectric coupling for dual-mode non-filamentary memristors." Applied Physics Reviews 9, no. 2 (June 2022): 021417. http://dx.doi.org/10.1063/5.0087624.

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Memristive devices and systems have emerged as powerful technologies to fuel neuromorphic chips. However, the traditional two-terminal memristor still suffers from nonideal device characteristics, raising challenges for its further application in versatile biomimetic emulation for neuromorphic computing owing to insufficient control of filament forming for filamentary-type cells and a transport barrier for interfacial switching cells. Here, we propose three-terminal memristors with a top-gate field-effect geometry by employing a ferroelectric material, poly(vinylidene fluoride–trifluoroethylene), as the dielectric layer. This approach can finely modulate ion transport and contact barrier at the switching interface in non-filamentary perovskite memristors, thus, creating two distinct operation modes (volatile and nonvolatile). Additionally, perovskite memristors show desirable resistive switching performance, including forming-free operation, high yield of 88.9%, cycle-to-cycle variation of 7.8%, and low operating current of sub-100 nA. The dual-mode memristor is capable of emulating biological nociception in both active (perceiving pain) and blocked states (suppressing pain signaling).

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Getty, N., T. Brettin, D. Jin, R. Stevens, and F. Xia. "Deep medical image analysis with representation learning and neuromorphic computing." Interface Focus 11, no. 1 (December 11, 2020): 20190122. http://dx.doi.org/10.1098/rsfs.2019.0122.

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Deep learning is increasingly used in medical imaging, improving many steps of the processing chain, from acquisition to segmentation and anomaly detection to outcome prediction. Yet significant challenges remain: (i) image-based diagnosis depends on the spatial relationships between local patterns, something convolution and pooling often do not capture adequately; (ii) data augmentation, the de facto method for learning three-dimensional pose invariance, requires exponentially many points to achieve robust improvement; (iii) labelled medical images are much less abundant than unlabelled ones, especially for heterogeneous pathological cases; and (iv) scanning technologies such as magnetic resonance imaging can be slow and costly, generally without online learning abilities to focus on regions of clinical interest. To address these challenges, novel algorithmic and hardware approaches are needed for deep learning to reach its full potential in medical imaging.

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Khajooei, Arash, Mohammad (Behdad) Jamshidi, and Shahriar B. Shokouhi. "A Super-Efficient TinyML Processor for the Edge Metaverse." Information 14, no. 4 (April 10, 2023): 235. http://dx.doi.org/10.3390/info14040235.

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Although the Metaverse is becoming a popular technology in many aspects of our lives, there are some drawbacks to its implementation on clouds, including long latency, security concerns, and centralized infrastructures. Therefore, designing scalable Metaverse platforms on the edge layer can be a practical solution. Nevertheless, the realization of these edge-powered Metaverse ecosystems without high-performance intelligent edge devices is almost impossible. Neuromorphic engineering, which employs brain-inspired cognitive architectures to implement neuromorphic chips and Tiny Machine Learning (TinyML) technologies, can be an effective tool to enhance edge devices in such emerging ecosystems. Thus, a super-efficient TinyML processor to use in the edge-enabled Metaverse platforms has been designed and evaluated in this research. This processor includes a Winner-Take-All (WTA) circuit that was implemented via a simplified Leaky Integrate and Fire (LIF) neuron on an FPGA. The WTA architecture is a computational principle in a neuromorphic system inspired by the mini-column structure in the human brain. The resource consumption of the WTA architecture is reduced by employing our simplified LIF neuron, making it suitable for the proposed edge devices. The results have indicated that the proposed neuron improves the response speed to almost 39% and reduces resource consumption by 50% compared to recent works. Using our simplified neuron, up to 4200 neurons can be deployed on VIRTEX 6 devices. The maximum operating frequency of the proposed neuron and our spiking WTA is 576.319 MHz and 514.095 MHz, respectively.

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Demin, V. A., A. V. Emelyanov, D. A. Lapkin, V. V. Erokhin, P. K. Kashkarov, and M. V. Kovalchuk. "Neuromorphic elements and systems as the basis for the physical implementation of artificial intelligence technologies." Crystallography Reports 61, no. 6 (November 2016): 992–1001. http://dx.doi.org/10.1134/s1063774516060067.

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Lakshmana Prabhu, Nagaraj, and Nagarajan Raghavan. "Computational Failure Analysis of Resistive RAM Used as a Synapse in a Convolutional Neural Network for Image Classification." EDFA Technical Articles 23, no. 1 (February 1, 2021): 29–33. http://dx.doi.org/10.31399/asm.edfa.2021-1.p029.

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Abstract Various NVM technologies are being explored for neuromorphic system realization, including resistive RAM, ferroelectric RAM, phase change RAM, spin transfer torque RAM, and NAND flash. This article discusses the potential of RRAM for such applications and evaluates key performance and reliability metrics in the context of neural network image classification. The authors conclude that the accuracy-power tradeoff may be further improved using alternative material stacks and multi-layer dielectrics so as to achieve better control of the oxygen vacancy or metallic filamentation process that governs RRAM switching characteristics.

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Samir N. Ajani,. "Frontiers of Computing - Evolutionary Trends and Cutting-Edge Technologies in Computer Science and Next Generation Application." Journal of Electrical Systems 20, no. 1s (March 28, 2024): 28–45. http://dx.doi.org/10.52783/jes.750.

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The cutting edge of computing is always changing because people are always looking for new ways to do things and combining cutting-edge tools. There are a number of natural trends that will affect the next generation of apps. Quantum computing is one of the most important trends. It is a new way of doing things that uses the rules of quantum physics to do calculations at speeds that are impossible with regular computers. Quantum computing could help solve hard issues in security, optimization, and modeling, which would lead to new science discoveries and technological progress.Neuromorphic computing, which is based on the structure of the human brain, is another new realm. The goal of this revolutionary way of computing is to create artificial neural networks that can learn, change, and process information in ways that are similar to how biological systems do it. There is a lot of promise for neuromorphic computing to improve machine learning and make AI apps more efficient and complex.The world of computing is also changing because artificial intelligence (AI) and edge computing are coming together. Edge computing brings computing power closer to the data source, which lowers delay and improves real-time processing. This is why it is so important for the growth of technologies like self-driving cars, smart cities, and the Internet of Things (IoT).Adding blockchain technology is another step forward that will make sure deals in open systems are safe and clear. Blockchain is used for more than just cryptocurrency. It has an impact on supply chain management, healthcare, and banking, among other areas. The quantum computing, neuromorphic computing, the combination of AI and edge computing, and the use of blockchain technology are all on the cutting edge of computing. Together, these trends bring computer science into a new era and lay the groundwork for groundbreaking uses that will shape the future of technology.

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Sueoka, Brandon, and Feng Zhao. "Memristive synaptic device based on a natural organic material—honey for spiking neural network in biodegradable neuromorphic systems." Journal of Physics D: Applied Physics 55, no. 22 (March 7, 2022): 225105. http://dx.doi.org/10.1088/1361-6463/ac585b.

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Abstract Spiking neural network (SNN) in future neuromorphic architectures requires hardware devices to be not only capable of emulating fundamental functionalities of biological synapse such as spike-timing dependent plasticity (STDP) and spike-rate dependent plasticity (SRDP), but also biodegradable to address current ecological challenges of electronic waste. Among different device technologies and materials, memristive synaptic devices based on natural organic materials have emerged as the favourable candidate to meet these demands. The metal–insulator-metal structure is analogous to biological synapse with low power consumption, fast switching speed and simulation of synaptic plasticity, while natural organic materials are water soluble, renewable and environmental friendly. In this study, the potential of a natural organic material—honey-based memristor for SNNs was demonstrated. The device exhibited forming-free bipolar resistive switching, a high switching speed of 100 ns set time and 500 ns reset time, STDP and SRDP learning behaviours, and dissolving in water. The intuitive conduction models for STDP and SRDP were proposed. These results testified that honey-based memristive synaptic devices are promising for SNN implementation in green electronics and biodegradable neuromorphic systems.

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Rahmeh, Samer, and Adam Neumann. "HUBO & QUBO and Prime Factorization." International Journal of Bioinformatics and Intelligent Computing 3, no. 1 (February 20, 2024): 45–69. http://dx.doi.org/10.61797/ijbic.v3i1.301.

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This document details the methodology and steps taken to convert Higher Order Unconstrained Binary Optimization (HUBO) models into Quadratic Unconstrained Binary Optimization (QUBO) models. The focus is primarily on prime factorization problems; a critical and computationally intensive task relevant in various domains including cryptography, optimization, and number theory. The conversion from Higher-Order Binary Optimization (HUBO) to Quadratic Unconstrained Binary Optimization (QUBO) models is crucial for harnessing the capabilities of advanced computing methodologies, particularly quantum computing and DYNEX neuromorphic computing. Quantum computing offers potential exponential speedups for specific problems through its intrinsic parallelism capabilities. Conversely, DYNEX neuromorphic computing enhances efficiency and accelerates the resolution of intricate, pattern-oriented tasks by simulating memristors in GPUs, employing a highly decentralized approach, via Blockchain technology. This transformation enables the exploitation of these cutting-edge computing paradigms to address complex optimization challenges effectively. Through detailed explanations, mathematical formulations, and algorithmic strategies, this document aims to provide a comprehensive guide to understanding and implementing the conversion process from HUBO to QUBO. It underscores the importance of such transformations in making prime factorization computationally feasible on both existing classical computers and emerging computing technologies.

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Guo, Pengfei, Andrew Sarangan, and Imad Agha. "A Review of Germanium-Antimony-Telluride Phase Change Materials for Non-Volatile Memories and Optical Modulators." Applied Sciences 9, no. 3 (February 4, 2019): 530. http://dx.doi.org/10.3390/app9030530.

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Chalcogenide phase change materials based on germanium-antimony-tellurides (GST-PCMs) have shown outstanding properties in non-volatile memory (NVM) technologies due to their high write and read speeds, reversible phase transition, high degree of scalability, low power consumption, good data retention, and multi-level storage capability. However, GST-based PCMs have shown recent promise in other domains, such as in spatial light modulation, beam steering, and neuromorphic computing. This paper reviews the progress in GST-based PCMs and methods for improving the performance within the context of new applications that have come to light in recent years.

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Ou, Qiao-Feng, Bang-Shu Xiong, Lei Yu, Jing Wen, Lei Wang, and Yi Tong. "In-Memory Logic Operations and Neuromorphic Computing in Non-Volatile Random Access Memory." Materials 13, no. 16 (August 10, 2020): 3532. http://dx.doi.org/10.3390/ma13163532.

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Recent progress in the development of artificial intelligence technologies, aided by deep learning algorithms, has led to an unprecedented revolution in neuromorphic circuits, bringing us ever closer to brain-like computers. However, the vast majority of advanced algorithms still have to run on conventional computers. Thus, their capacities are limited by what is known as the von-Neumann bottleneck, where the central processing unit for data computation and the main memory for data storage are separated. Emerging forms of non-volatile random access memory, such as ferroelectric random access memory, phase-change random access memory, magnetic random access memory, and resistive random access memory, are widely considered to offer the best prospect of circumventing the von-Neumann bottleneck. This is due to their ability to merge storage and computational operations, such as Boolean logic. This paper reviews the most common kinds of non-volatile random access memory and their physical principles, together with their relative pros and cons when compared with conventional CMOS-based circuits (Complementary Metal Oxide Semiconductor). Their potential application to Boolean logic computation is then considered in terms of their working mechanism, circuit design and performance metrics. The paper concludes by envisaging the prospects offered by non-volatile devices for future brain-inspired and neuromorphic computation.

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Zhu, Minglu, Tianyiyi He, and Chengkuo Lee. "Technologies toward next generation human machine interfaces: From machine learning enhanced tactile sensing to neuromorphic sensory systems." Applied Physics Reviews 7, no. 3 (September 2020): 031305. http://dx.doi.org/10.1063/5.0016485.

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Wan, Changjin, Mengjiao Pei, Kailu Shi, Hangyuan Cui, Haotian Long, Lesheng Qiao, Qianye Xing, and Qing Wan. "Toward a Brain‐Neuromorphics Interface." Advanced Materials, February 10, 2024. http://dx.doi.org/10.1002/adma.202311288.

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AbstractBrain‐computer interfaces (BCIs) that enable human‐machine interaction have immense potential in restoring or augmenting human capabilities. Traditional BCIs are realized based on complementary metal‐oxide‐semiconductor (CMOS) technologies with complex, bulky, and low biocompatible circuits, and suffer with the low energy efficiency of the von Neumann architecture. The brain‐neuromorphics interface (BNI) would offer a promising solution to advance the BCI technologies and shape our interactions with machineries. Neuromorphic devices and systems are able to provide substantial computation power with extremely high energy‐efficiency by implementing in‐materia computing such as in situ vector‐matrix multiplication (VMM) and physical reservoir computing. Recent progresses on integrating neuromorphic components with sensing and/or actuating modules, give birth to the neuromorphic afferent nerve, efferent nerve, sensorimotor loop, and so on, which has advanced the technologies for future neurorobotics by achieving sophisticated sensorimotor capabilities as the biological system. With the development on the compact artificial spiking neuron and bioelectronic interfaces, the seamless communication between a BNI and a bioentity is reasonably expectable. In this review, the upcoming BNIs are profiled by introducing the brief history of neuromorphics, reviewing the recent progresses on related areas, and discussing the future advances and challenges that lie ahead.This article is protected by copyright. All rights reserved

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"Vision Technologies for Smartphones." New Electronics 56, no. 3 (March 2023): 31. http://dx.doi.org/10.12968/s0047-9624(23)60547-4.

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Bartolozzi, Chiara, Giacomo Indiveri, and Elisa Donati. "Embodied neuromorphic intelligence." Nature Communications 13, no. 1 (February 23, 2022). http://dx.doi.org/10.1038/s41467-022-28487-2.

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AbstractThe design of robots that interact autonomously with the environment and exhibit complex behaviours is an open challenge that can benefit from understanding what makes living beings fit to act in the world. Neuromorphic engineering studies neural computational principles to develop technologies that can provide a computing substrate for building compact and low-power processing systems. We discuss why endowing robots with neuromorphic technologies – from perception to motor control – represents a promising approach for the creation of robots which can seamlessly integrate in society. We present initial attempts in this direction, highlight open challenges, and propose actions required to overcome current limitations.

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Cramer, Benjamin, Sebastian Billaudelle, Simeon Kanya, Aron Leibfried, Andreas Grübl, Vitali Karasenko, Christian Pehle, et al. "Surrogate gradients for analog neuromorphic computing." Proceedings of the National Academy of Sciences 119, no. 4 (January 14, 2022). http://dx.doi.org/10.1073/pnas.2109194119.

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Significance Neuromorphic systems aim to accomplish efficient computation in electronics by mirroring neurobiological principles. Taking advantage of neuromorphic technologies requires effective learning algorithms capable of instantiating high-performing neural networks, while also dealing with inevitable manufacturing variations of individual components, such as memristors or analog neurons. We present a learning framework resulting in bioinspired spiking neural networks with high performance, low inference latency, and sparse spike-coding schemes, which also self-corrects for device mismatch. We validate our approach on the BrainScaleS-2 analog spiking neuromorphic system, demonstrating state-of-the-art accuracy, low latency, and energy efficiency. Our work sketches a path for building powerful neuromorphic processors that take advantage of emerging analog technologies.

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Moss, David. "Photonic Multiplexing Technologies for Optical Neuromorphic Networks." SSRN Electronic Journal, 2022. http://dx.doi.org/10.2139/ssrn.4204530.

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Shen, Jiabin, Zengguang Cheng, and Peng Zhou. "Optical and optoelectronic neuromorphic devices based on emerging memory technologies." Nanotechnology, May 23, 2022. http://dx.doi.org/10.1088/1361-6528/ac723f.

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Abstract As artificial intelligence continues its rapid development, inevitable challenges arise for the mainstream computing hardware to process voluminous data (Big data). The conventional computer system based on von Neumann architecture with separated processor unit and memory is approaching the limit of computational speed and energy efficiency. Thus, novel computing architectures such as in-memory computing and neuromorphic computing based on emerging memory technologies have been proposed. In recent years, light is incorporated into computational devices, beyond the data transmission in traditional optical communications, due to its innate superiority in speed, bandwidth, energy efficiency, etc. Thereinto, photo-assisted and photoelectrical synapses are developed for neuromorphic computing. Additionally, both the storage and readout processes can be implemented in optical domain in some emerging photonic devices to leverage unique properties of photonics. In this review, we introduce typical photonic neuromorphic devices rooted from emerging memory technologies together with corresponding operational mechanisms. In the end, the advantages and limitations of these devices originated from different modulation means are listed and discussed.

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Donati, Elisa, and Giacomo Valle. "Neuromorphic hardware for somatosensory neuroprostheses." Nature Communications 15, no. 1 (January 16, 2024). http://dx.doi.org/10.1038/s41467-024-44723-3.

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AbstractIn individuals with sensory-motor impairments, missing limb functions can be restored using neuroprosthetic devices that directly interface with the nervous system. However, restoring the natural tactile experience through electrical neural stimulation requires complex encoding strategies. Indeed, they are presently limited in effectively conveying or restoring tactile sensations by bandwidth constraints. Neuromorphic technology, which mimics the natural behavior of neurons and synapses, holds promise for replicating the encoding of natural touch, potentially informing neurostimulation design. In this perspective, we propose that incorporating neuromorphic technologies into neuroprostheses could be an effective approach for developing more natural human-machine interfaces, potentially leading to advancements in device performance, acceptability, and embeddability. We also highlight ongoing challenges and the required actions to facilitate the future integration of these advanced technologies.

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Liu, Xuerong, Cui Sun, Xiaoyu Ye, Xiaojian Zhu, Cong Hu, Hongwei Tan, Shang He, Mengjie Shao, and Run‐Wei Li. "Neuromorphic Nanoionics for human‐machine Interaction: from Materials to Applications." Advanced Materials, February 29, 2024. http://dx.doi.org/10.1002/adma.202311472.

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AbstractHuman‐machine interaction (HMI) technology has undergone significant advancements in recent years, enabling seamless communication between humans and machines. Its expansion has extended into various emerging domains, including human healthcare, machine perception, and biointerfaces, thereby magnifying the demand for advanced intelligent technologies. Neuromorphic computing, a paradigm rooted in nanoionic devices that emulate the operations and architecture of the human brain, has emerged as a powerful tool for highly efficient information processing. This paper delivers a comprehensive review of recent developments in nanoionic device‐based neuromorphic computing technologies and their pivotal role in shaping the next‐generation of HMI. Through a detailed examination of fundamental mechanisms and behaviors, the paper explores the ability of nanoionic memristors and ion‐gated transistors to emulate the intricate functions of neurons and synapses. Crucial performance metrics, such as reliability, energy efficiency, flexibility, and biocompatibility, are rigorously evaluated. Potential applications, challenges, and opportunities of using the neuromorphic computing technologies in emerging HMI technologies, are discussed and outlooked, shedding light on the fusion of humans with machines.This article is protected by copyright. All rights reserved

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Zhou, Kui, Ziqi Jia, Xin-Qi Ma, Wenbiao Niu, Yao Zhou, Ning Huang, Guanglong Ding, et al. "Manufacturing of graphene based synaptic devices for optoelectronic applications." International Journal of Extreme Manufacturing, August 8, 2023. http://dx.doi.org/10.1088/2631-7990/acee2e.

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Abstract Neuromorphic computing systems can perform memory and computing tasks in parallel on artificial synaptic devices through simulating synaptic functions, which is promising for breaking through the limitations of conventional von Neumann bottlenecks at hardware level. Artificial optoelectronic synapses enable the coupling of optical and electrical signals in synaptic modulation, which opens up an innovative path for effective neuromorphic systems. With the advantages of high mobility, optical transparency, ultrawideband tunability, and environmental stability, graphene has attracted tremendous interest for electronic and optoelectronic applications. Recent research results highlight the significance of implementing graphene into artificial synaptic devices. Herein, to better demonstrate the potential of graphene-based synaptic devices, the fabrication technologies of graphene are first presented. Then, the roles of graphene in various synaptic devices are explained. Furthermore, their typical optoelectronic applications in neuromorphic systems are reviewed. Finally, outlooks for development of synaptic devices based on graphene are also proposed. This review will provide a comprehensive understanding of graphene fabrication technologies and graphene-based synaptic device for optoelectronic applications, also present an outlook for development of graphene-based synaptic device in future neuromorphic systems.

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Bai, Yunping, Xingyuan Xu, Mengxi Tan, Yang Sun, Yang Li, Jiayang Wu, Roberto Morandotti, Arnan Mitchell, Kun Xu, and David J. Moss. "Photonic multiplexing techniques for neuromorphic computing." Nanophotonics, January 9, 2023. http://dx.doi.org/10.1515/nanoph-2022-0485.

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Abstract The simultaneous advances in artificial neural networks and photonic integration technologies have spurred extensive research in optical computing and optical neural networks (ONNs). The potential to simultaneously exploit multiple physical dimensions of time, wavelength and space give ONNs the ability to achieve computing operations with high parallelism and large-data throughput. Different photonic multiplexing techniques based on these multiple degrees of freedom have enabled ONNs with large-scale interconnectivity and linear computing functions. Here, we review the recent advances of ONNs based on different approaches to photonic multiplexing, and present our outlook on key technologies needed to further advance these photonic multiplexing/hybrid-multiplexing techniques of ONNs.

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Park, Jaeseoung, Ashwani Kumar, Yucheng Zhou, Sangheon Oh, Jeong-Hoon Kim, Yuhan Shi, Soumil Jain, et al. "Multi-level, forming and filament free, bulk switching trilayer RRAM for neuromorphic computing at the edge." Nature Communications 15, no. 1 (April 25, 2024). http://dx.doi.org/10.1038/s41467-024-46682-1.

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AbstractCMOS-RRAM integration holds great promise for low energy and high throughput neuromorphic computing. However, most RRAM technologies relying on filamentary switching suffer from variations and noise, leading to computational accuracy loss, increased energy consumption, and overhead by expensive program and verify schemes. We developed a filament-free, bulk switching RRAM technology to address these challenges. We systematically engineered a trilayer metal-oxide stack and investigated the switching characteristics of RRAM with varying thicknesses and oxygen vacancy distributions to achieve reliable bulk switching without any filament formation. We demonstrated bulk switching at megaohm regime with high current nonlinearity, up to 100 levels without compliance current. We developed a neuromorphic compute-in-memory platform and showcased edge computing by implementing a spiking neural network for an autonomous navigation/racing task. Our work addresses challenges posed by existing RRAM technologies and paves the way for neuromorphic computing at the edge under strict size, weight, and power constraints.

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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 an overview of recent neuromorphic advancements in medicine, including medical imaging and cancer diagnosis, processing of biosignals for diagnosis, and biomedical interfaces, such as motor, cognitive, and perception prostheses. For each section, we provide examples of how brain-inspired models can successfully compete with conventional artificial intelligence algorithms, demonstrating the potential of neuromorphic engineering to meet demands and improve patient outcomes. Lastly, we discuss current struggles in fitting neuromorphic hardware with non-neuromorphic technologies and propose potential solutions for future bottlenecks in hardware compatibility.

Journal articles on the topic 'Neuromorphic technologies'

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