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Journal articles on the topic 'Neuromorphic computer systems'

Author: Grafiati

Published: 30 May 2022

Last updated: 19 July 2025

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

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As technologies like the Internet, artificial intelligence, and big data evolve at a rapid pace, computer architecture is transitioning from compute-intensive to memory-intensive. However, traditional von Neumann architectures encounter bottlenecks in addressing modern computational challenges. The emulation of the behaviors of a synapse at the device level by ionic/electronic devices has shown promising potential in future neural-inspired and compact artificial intelligence systems. To address these issues, this review thoroughly investigates the recent progress in metal-oxide 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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Satnam Singh, Ishita Sabharwal, Shweta Kushwaha, Dr. Shilpi Jain, and Dr. Madhur Jain. "Enhancing Human-Machine Interaction: Leveraging Neuromorphic Chips for Adaptive Learning and Control in Neural Prosthetics and Artificial Intelligence." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 6 (2024): 933–40. http://dx.doi.org/10.32628/cseit241061135.

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This paper examines the integration of neuromorphic chips, AI, and neural prostheses to enhance human-machine interaction. Neuromorphic chips, modelled after the brain's neural architecture, enable efficient learning, adaptive behaviour, and energy-efficient processing in AI systems and prostheses. These chips improve pattern recognition, adaptive control, and integration with the human nervous system. In neural prostheses, they promise seamless brain-computer interfaces (BCI) to restore mobility for paralyzed individuals and enable precise control of devices for people with severe disabilities. For AI systems, neuromorphic chips support rapid learning from large datasets, enabling adaptability in dynamic environments and real-time decision-making.

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

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This paper introduces a multidisciplinary conceptual perspective encompassing artificial intelligence (AI), artificial general intelligence (AGI), and cybernetics, framed within what we call the formalism of generalized neuromorphism. Drawing from recent advancements in computing, such as neuromorphic computing and spiking neural networks, as well as principles from the theory of open dynamical systems and stochastic classical and quantum dynamics, this formalism is tailored to model generic networks comprising abstract processing events. A pivotal aspect of our approach is the incorporation 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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Sharma, Parul, Balwinder Raj, and Sandeep Singh Gill. "Spintronics Based Non-Volatile MRAM for Intelligent Systems." International Journal on Semantic Web and Information Systems 18, no. 1 (2022): 1–16. http://dx.doi.org/10.4018/ijswis.310056.

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In this paper the spintronic-based memory MRAM is presented that showed how it can replace both SRAM and DRAM and provide the high speed with great chip size. Moreover, MRAM is the nonvolatile memory that provides great advancement in the storage process. The different types of MRAM are mentioned with the techniques used for writing purpose and also mention which one is more used and why. The basic working principle and the function performed by the MRAM are discussed. Artificial intelligence (AI) is mentioned with its pros and cons for intelligent systems. Neuromorphic computing is also explained along with its important role in intelligent systems. Some reasons are also discussed as to why neuromorphic computing is so important. This paper also presents how spintronic-based devices especially memory can be used in intelligent systems and neuromorphic computing. Nanoscale spintronic-based MRAM plays a key role in intelligent systems and neuromorphic computing applications.

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Zhou, Jun. "Recent Progress of Memristor-based Neuromorphic Computing." Transactions on Computer Science and Intelligent Systems Research 5 (August 12, 2024): 1655–61. http://dx.doi.org/10.62051/1kany131.

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The evolution of memristors and their successful applications have positioned them as formidable candidates for the next generation of computer systems. With the rapid advancement of foundational ar- tificial intelligence applications, there is an increasing demand for computational power, energy efficiency, and stability. Memristors and the Neuromorphic Computing (NMC) systems they underpin hold signifi- can’t potential to break through the von Neumann bottleneck. However, technical challenges remain in the application of NMC to computer systems. In this review, we focus on the performance of various structured memristors within Neuromorphic Computing and across different machine learning algorithms. We pro- vide an overview of the current challenges faced by NMC, including the structural limitations due to sneak paths and the inherent power consumption limitations, and offer a perspective on future developments and opportunities in the field.

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K P, VISHNUPRIYA, JWALA JOSE, PRINCE JOY, SRITHA S, and GIBI K. S. "Brain-Inspired Artificial Intelligence: Revolutionizing Computing and Cognitive Systems." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–8. https://doi.org/10.55041/ijsrem39825.

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Brain-inspired artificial intelligence (AI) is a rapidly evolving field that seeks to model computational systems after the structure, processes, and functioning of the human brain. By drawing from neuroscience and cognitive science, brain-inspired AI aims to improve the efficiency, scalability, and adaptability of machine learning algorithms. This paper explores the key technologies and advancements in the realm of brain-inspired AI, including neural networks, neuromorphic hardware, brain-computer interfaces, and algorithms inspired by biological learning mechanisms. Additionally, we will analyze the challenges and future opportunities in achieving more brain-like cognitive systems. The integration of these technologies promises a paradigm shift in AI research, bringing us closer to artificial general intelligence (AGI) while creating more energy-efficient and resilient systems. Keywords Brain-inspired AI, Neural Networks, Neuromorphic Computing, Spiking Neural Networks, Artificial General Intelligence, Brain-Computer Interfaces, Cognitive Architectures.

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Dunham, Christopher S., Sam Lilak, Joel Hochstetter, et al. "Nanoscale neuromorphic networks and criticality: a perspective." Journal of Physics: Complexity 2, no. 4 (2021): 042001. http://dx.doi.org/10.1088/2632-072x/ac3ad3.

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Abstract Numerous studies suggest critical dynamics may play a role in information processing and task performance in biological systems. However, studying critical dynamics in these systems can be challenging due to many confounding biological variables that limit access to the physical processes underpinning critical dynamics. Here we offer a perspective on the use of abiotic, neuromorphic nanowire networks as a means to investigate critical dynamics in complex adaptive systems. Neuromorphic nanowire networks are composed of metallic nanowires and possess metal-insulator-metal junctions. These networks self-assemble into a highly interconnected, variable-density structure and exhibit nonlinear electrical switching properties and information processing capabilities. We highlight key dynamical characteristics observed in neuromorphic nanowire networks, including persistent fluctuations in conductivity with power law distributions, hysteresis, chaotic attractor dynamics, and avalanche criticality. We posit that neuromorphic nanowire networks can function effectively as tunable abiotic physical systems for studying critical dynamics and leveraging criticality for computation.

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Siddique, Ali, Jingqi Sun, Kung Jui Hou, Mang I. Vai, Sio Hang Pun, and Muhammad Azhar Iqbal. "SpikoPoniC: A Low-Cost Spiking Neuromorphic Computer for Smart Aquaponics." Agriculture 13, no. 11 (2023): 2057. http://dx.doi.org/10.3390/agriculture13112057.

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Aquaponics is an emerging area of agricultural sciences that combines aquaculture and hydroponics in a symbiotic way to enhance crop production. A stable smart aquaponic system requires estimating the fish size in real time. Though deep learning has shown promise in the context of smart aquaponics, most smart systems are extremely slow and costly and cannot be deployed on a large scale. Therefore, we design and present a novel neuromorphic computer that uses spiking neural networks (SNNs) for estimating not only the length but also the weight of the fish. To train the SNN, we present a novel hybrid scheme in which some of the neural layers are trained using direct SNN backpropagation, while others are trained using standard backpropagation. By doing this, a blend of high hardware efficiency and accuracy can be achieved. The proposed computer SpikoPoniC can classify more than 84 million fish samples in a second, achieving a speedup of at least 3369× over traditional general-purpose computers. The SpikoPoniC consumes less than 1100 slice registers on Virtex 6 and is much cheaper than most SNN-based hardware systems. To the best of our knowledge, this is the first SNN-based neuromorphic system that performs smart real-time aquaponic monitoring.

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Jang, Taejin, Suhyeon Kim, Jeesoo Chang, et al. "3D AND-Type Stacked Array for Neuromorphic Systems." Micromachines 11, no. 9 (2020): 829. http://dx.doi.org/10.3390/mi11090829.

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NOR/AND flash memory was studied in neuromorphic systems to perform vector-by-matrix multiplication (VMM) by summing the current. Because the size of NOR/AND cells exceeds those of other memristor synaptic devices, we proposed a 3D AND-type stacked array to reduce the cell size. Through a tilted implantation method, the conformal sources and drains of each cell could be formed, with confirmation by a technology computer aided design (TCAD) simulation. In addition, the cell-to-cell variation due to the etch slope could be eliminated by controlling the deposition thickness of the cells. The suggested array can be beneficial in simple program/inhibit schemes given its use of Fowler–Nordheim (FN) tunneling because the drain lines and source lines are parallel. Therefore, the conductance of each synaptic device can be updated at low power level.

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Ferreira de Lima, Thomas, Alexander N. Tait, Armin Mehrabian, et al. "Primer on silicon neuromorphic photonic processors: architecture and compiler." Nanophotonics 9, no. 13 (2020): 4055–73. http://dx.doi.org/10.1515/nanoph-2020-0172.

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AbstractMicroelectronic computers have encountered challenges in meeting all of today’s demands for information processing. Meeting these demands will require the development of unconventional computers employing alternative processing models and new device physics. Neural network models have come to dominate modern machine learning algorithms, and specialized electronic hardware has been developed to implement them more efficiently. A silicon photonic integration industry promises to bring manufacturing ecosystems normally reserved for microelectronics to photonics. Photonic devices have already found simple analog signal processing niches where electronics cannot provide sufficient bandwidth and reconfigurability. In order to solve more complex information processing problems, they will have to adopt a processing model that generalizes and scales. Neuromorphic photonics aims to map physical models of optoelectronic systems to abstract models of neural networks. It represents a new opportunity for machine information processing on sub-nanosecond timescales, with application to mathematical programming, intelligent radio frequency signal processing, and real-time control. The strategy of neuromorphic engineering is to externalize the risk of developing computational theory alongside hardware. The strategy of remaining compatible with silicon photonics externalizes the risk of platform development. In this perspective article, we provide a rationale for a neuromorphic photonics processor, envisioning its architecture and a compiler. We also discuss how it can be interfaced with a general purpose computer, i.e. a CPU, as a coprocessor to target specific applications. This paper is intended for a wide audience and provides a roadmap for expanding research in the direction of transforming neuromorphic photonics into a viable and useful candidate for accelerating neuromorphic computing.

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Choi, Hyun-Seok, Yu Jeong Park, Jong-Ho Lee, and Yoon Kim. "3-D Synapse Array Architecture Based on Charge-Trap Flash Memory for Neuromorphic Application." Electronics 9, no. 1 (2019): 57. http://dx.doi.org/10.3390/electronics9010057.

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In order to address a fundamental bottleneck of conventional digital computers, there is recently a tremendous upsurge of investigations on hardware-based neuromorphic systems. To emulate the functionalities of artificial neural networks, various synaptic devices and their 2-D cross-point array structures have been proposed. In our previous work, we proposed the 3-D synapse array architecture based on a charge-trap flash (CTF) memory. It has the advantages of high-density integration of 3-D stacking technology and excellent reliability characteristics of mature CTF device technology. This paper examines some issues of the 3-D synapse array architecture. Also, we propose an improved structure and programming method compared to the previous work. The synaptic characteristics of the proposed method are closely examined and validated through a technology computer-aided design (TCAD) device simulation and a system-level simulation for the pattern recognition task. The proposed technology will be the promising solution for high-performance and high-reliability of neuromorphic hardware systems.

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Liu, Te-Yuan, Ata Mahjoubfar, Daniel Prusinski, and Luis Stevens. "Neuromorphic computing for content-based image retrieval." PLOS ONE 17, no. 4 (2022): e0264364. http://dx.doi.org/10.1371/journal.pone.0264364.

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Neuromorphic computing mimics the neural activity of the brain through emulating spiking neural networks. In numerous machine learning tasks, neuromorphic chips are expected to provide superior solutions in terms of cost and power efficiency. Here, we explore the application of Loihi, a neuromorphic computing chip developed by Intel, for the computer vision task of image retrieval. We evaluated the functionalities and the performance metrics that are critical in content-based visual search and recommender systems using deep-learning embeddings. Our results show that the neuromorphic solution is about 2.5 times more energy-efficient compared with an ARM Cortex-A72 CPU and 12.5 times more energy-efficient compared with NVIDIA T4 GPU for inference by a lightweight convolutional neural network when batch size is 1 while maintaining the same level of matching accuracy. The study validates the potential of neuromorphic computing in low-power image retrieval, as a complementary paradigm to the existing von Neumann architectures.

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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 (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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Demcheva, Alexandra, Anton Korsakov, Ivan Fomin, Aleksandr Bakhshiev, and Ekaterina Smirnova. "Prevention of emergency situations in complex technical systems using a neuromorphic approach." Robotics and Technical Cybernetics 11, no. 4 (2023): 281–91. http://dx.doi.org/10.31776/rtcj.11405.

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The paper proposes a scheme of emergency prevention system based on neuromorphic approach. The system includes a prediction unit that implements a mathematical model of the cerebellum predictive functions, and an alarm unit that implements the pain sensation model, proposed by the authors earlier. As a basic element of the proposed system the Compartmental Spiking Neuron Model (CSNM) was used, capable of learning from a small number of examples. The use of neuromorphic approach allows to overcome the limitations associated with the formalizing complexity of the systems being diagnosed and the low availability of data for modeling the processes occurring in them. The overcoming these limitations is possible due to the possibility of learning from a small number of examples and the absence of the need to model the system being diagnosed itself. The paper also presents the results of testing of the proposed scheme, which was carried out on a computer model using synthetic data. The results of the testing showed the fundamental applicability of the proposed scheme in neuromorphic control systems.

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Mougkogiannis, Panagiotis, and Andrew Adamatzky. "The Effects of Omeprazole on the Neuron-like Spiking of the Electrical Potential of Proteinoid Microspheres." Molecules 29, no. 19 (2024): 4700. http://dx.doi.org/10.3390/molecules29194700.

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This study examines a new approach to hybrid neuromorphic devices by studying the impact of omeprazole–proteinoid complexes on Izhikevich neuron models. We investigate the influence of these metabolic structures on five specific patterns of neuronal firing: accommodation, chattering, triggered spiking, phasic spiking, and tonic spiking. By combining omeprazole, a proton pump inhibitor, with proteinoids, we create a unique substrate that interfaces with neuromorphic models. The Izhikevich neuron model is used because it is computationally efficient and can accurately simulate the various behaviours of cortical neurons. The results of our simulations show that omeprazole–proteinoid complexes have the ability to affect neuronal dynamics in different ways. This suggests that they could be used as adjustable components in bio-inspired computer systems. We noticed a notable alteration in the frequency of spikes, patterns of bursts, and rates of adaptation, especially in chattering and triggered spiking behaviours. The findings indicate that omeprazole–proteinoid complexes have the potential to serve as adaptable elements in neuromorphic systems, presenting novel opportunities for information processing and computation that have origins in neurobiological principles. This study makes a valuable contribution to the expanding field of biochemical neuromorphic devices and establishes a basis for the development of hybrid bio-synthetic computational systems.

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Abbas, A. H., Hend Abdel-Ghani, and Ivan S. Maksymov. "Classical and Quantum Physical Reservoir Computing for Onboard Artificial Intelligence Systems: A Perspective." Dynamics 4, no. 3 (2024): 643–70. http://dx.doi.org/10.3390/dynamics4030033.

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Artificial intelligence (AI) systems of autonomous systems such as drones, robots and self-driving cars may consume up to 50% of the total power available onboard, thereby limiting the vehicle’s range of functions and considerably reducing the distance the vehicle can travel on a single charge. Next-generation onboard AI systems need an even higher power since they collect and process even larger amounts of data in real time. This problem cannot be solved using traditional computing devices since they become more and more power-consuming. In this review article, we discuss the perspectives on the development of onboard neuromorphic computers that mimic the operation of a biological brain using the nonlinear–dynamical properties of natural physical environments surrounding autonomous vehicles. Previous research also demonstrated that quantum neuromorphic processors (QNPs) can conduct computations with the efficiency of a standard computer while consuming less than 1% of the onboard battery power. Since QNPs are a semi-classical technology, their technical simplicity and low cost compared to quantum computers make them ideally suited for applications in autonomous AI systems. Providing a perspective on the future progress in unconventional physical reservoir computing and surveying the outcomes of more than 200 interdisciplinary research works, this article will be of interest to a broad readership, including both students and experts in the fields of physics, engineering, quantum technologies and computing.

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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 (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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M L Sharma, Neelam Sharma, Sunil Kumar, et al. "Breaking bottlenecks: CPU optimization through architectural and neuromorphic techniques." World Journal of Advanced Research and Reviews 26, no. 2 (2025): 190–204. https://doi.org/10.30574/wjarr.2025.26.2.1463.

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This research explores two different approaches to improving how computers process information efficiently. The first part uses the Gem5 simulator to test and compare three types of CPU designs—Timing Simple CPU, Minor CPU, and O3CPU—by running a basic program. We looked at how features like pipelining, caching, and branch prediction affect how fast the program runs and how efficiently the CPU works. The second part focuses on recognizing handwritten digits from the MNIST dataset using two types of AI models. One model is a traditional neural network (MLP) that runs on a standard computer setup (Von Neumann architecture), and the other is a spiking neural network (SNN) that runs on a neuromorphic system, which mimics how the human brain works. Overall, this study shows how both architectural improvements and brain-inspired computing can help solve performance and efficiency issues in modern computing systems.

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Hughes, Mark A., Mike J. Shipston, and Alan F. Murray. "Towards a ‘siliconeural computer’: technological successes and challenges." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 373, no. 2046 (2015): 20140217. http://dx.doi.org/10.1098/rsta.2014.0217.

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Electronic signals govern the function of both nervous systems and computers, albeit in different ways. As such, hybridizing both systems to create an iono-electric brain–computer interface is a realistic goal; and one that promises exciting advances in both heterotic computing and neuroprosthetics capable of circumventing devastating neuropathology. ‘Neural networks’ were, in the 1980s, viewed naively as a potential panacea for all computational problems that did not fit well with conventional computing. The field bifurcated during the 1990s into a highly successful and much more realistic machine learning community and an equally pragmatic, biologically oriented ‘neuromorphic computing’ community. Algorithms found in nature that use the non-synchronous, spiking nature of neuronal signals have been found to be (i) implementable efficiently in silicon and (ii) computationally useful. As a result, interest has grown in techniques that could create mixed ‘siliconeural’ computers. Here, we discuss potential approaches and focus on one particular platform using parylene-patterned silicon dioxide.

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Young, Aaron R., Mark E. Dean, James S. Plank, and Garrett S. Rose. "A Review of Spiking Neuromorphic Hardware Communication Systems." IEEE Access 7 (2019): 135606–20. http://dx.doi.org/10.1109/access.2019.2941772.

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Chung, Jaeyong, Taehwan Shin, and Joon-Sung Yang. "Simplifying Deep Neural Networks for FPGA-Like Neuromorphic Systems." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 38, no. 11 (2019): 2032–42. http://dx.doi.org/10.1109/tcad.2018.2877016.

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Kang, Yongshin, Joon-Sung Yang, and Jaeyong Chung. "Weight Partitioning for Dynamic Fixed-Point Neuromorphic Computing Systems." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 38, no. 11 (2019): 2167–71. http://dx.doi.org/10.1109/tcad.2018.2878167.

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Cazzato, Dario, and Flavio Bono. "An Application-Driven Survey on Event-Based Neuromorphic Computer Vision." Information 15, no. 8 (2024): 472. http://dx.doi.org/10.3390/info15080472.

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Traditional frame-based cameras, despite their effectiveness and usage in computer vision, exhibit limitations such as high latency, low dynamic range, high power consumption, and motion blur. For two decades, researchers have explored neuromorphic cameras, which operate differently from traditional frame-based types, mimicking biological vision systems for enhanced data acquisition and spatio-temporal resolution. Each pixel asynchronously captures intensity changes in the scene above certain user-defined thresholds, and streams of events are captured. However, the distinct characteristics of these sensors mean that traditional computer vision methods are not directly applicable, necessitating the investigation of new approaches before being applied in real applications. This work aims to fill existing gaps in the literature by providing a survey and a discussion centered on the different application domains, differentiating between computer vision problems and whether solutions are better suited for or have been applied to a specific field. Moreover, an extensive discussion highlights the major achievements and challenges, in addition to the unique characteristics, of each application field.

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Borra, Rajeev. "Neuromorphic Computing: Bridging Biological Intelligence and Artificial Intelligence." International Journal of Engineering and Advanced Technology 14, no. 2 (2024): 19–24. https://doi.org/10.35940/ijeat.b4558.14021224.

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Neuromorphic computing represents a groundbreaking paradigm shift in the realm of artificial intelligence, aiming to replicate the architecture and operational mechanisms of the human brain. This paper provides a comprehensive exploration of the foundational principles that underpin this innovative approach, examining the technological implementations that are driving advancements in the field. We delve into a diverse array of applications across various sectors, highlighting the versatility and relevance of neuromorphic systems. Key challenges such as scalability, integration with existing technologies, and the complexity of accurately modeling intricate brain functions are thoroughly analyzed. The discussion includes potential solutions and future prospects, illuminating pathways to overcome these obstacles. To illustrate the tangible impact of these technologies, we present practical examples that underscore their transformative potential in domains such as robotics, where they enable adaptive learning and autonomy, healthcare, where they enhance diagnostic tools and personalized medicine; cognitive computing, which facilitates improved human-computer interaction; and the development of smart cities, optimizing urban infrastructure and resource management. Through this examination, the paper aims to underscore the significance of neuromorphic computing in shaping the future of intelligent systems and fostering a deeper understanding of both artificial and natural intelligence.

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Rajeev, Borra. "Neuromorphic Computing: Bridging Biological Intelligence and Artificial Intelligence." International Journal of Engineering and Advanced Technology (IJEAT) 14, no. 2 (2024): 19–24. https://doi.org/10.35940/ijeat.B4558.14021224.

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<strong>Abstract:</strong> Neuromorphic computing represents a groundbreaking paradigm shift in the realm of artificial intelligence, aiming to replicate the architecture and operational mechanisms of the human brain. This paper provides a comprehensive exploration of the foundational principles that underpin this innovative approach, examining the technological implementations that are driving advancements in the field. We delve into a diverse array of applications across various sectors, highlighting the versatility and relevance of neuromorphic systems. Key challenges such as scalability, integration with existing technologies, and the complexity of accurately modeling intricate brain functions are thoroughly analyzed. The discussion includes potential solutions and future prospects, illuminating pathways to overcome these obstacles. To illustrate the tangible impact of these technologies, we present practical examples that underscore their transformative potential in domains such as robotics, where they enable adaptive learning and autonomy; healthcare, where they enhance diagnostic tools and personalized medicine; cognitive computing, which facilitates improved human-computer interaction; and the development of smart cities, optimizing urban infrastructure and resource management. Through this examination, the paper aims to underscore the significance of neuromorphic computing in shaping the future of intelligent systems and fostering a deeper understanding of both artificial and natural intelligence.

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Shchanikov, Sergey, Ilya Bordanov, Alexey Kucherik, Evgeny Gryaznov, and Alexey Mikhaylov. "Neuromorphic Analog Machine Vision Enabled by Nanoelectronic Memristive Devices." Applied Sciences 13, no. 24 (2023): 13309. http://dx.doi.org/10.3390/app132413309.

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Arrays of memristive devices coupled with photosensors can be used for capturing and processing visual information, thereby realizing the concept of “in-sensor computing”. This is a promising concept associated with the development of compact and low-power machine vision devices, which is crucial important for bionic prostheses of eyes, on-board image recognition systems for unmanned vehicles, computer vision in robotics, etc. This concept can be applied for the creation of a memristor based neuromorphic analog machine vision systems, and here, we propose a new architecture for these systems in which captured visual data are fed to a spiking artificial neural network (SNN) based on memristive devices without analog-to-digital and digital-to-analog conversions. Such an approach opens up the opportunities of creating more compact, energy-efficient visual processing units for wearable, on-board, and embedded electronics for such areas as robotics, the Internet of Things, and neuroprosthetics, as well as other practical applications in the field of artificial intelligence.

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

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Over the last decade, artificial intelligence (AI) has found many applications areas in the society. As AI solutions have become more sophistication and the use cases grew, they highlighted the need to address performance and energy efficiency challenges faced during the implementation process. To address these challenges, there has been growing interest in neuromorphic chips. Neuromorphic computing relies on non von Neumann architectures as well as novel devices, circuits and manufacturing technologies to mimic the human brain. Among such technologies, three-dimensional (3D) integration is an 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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Chen, Guang, Jian Cao, Chenglong Zou, et al. "PAIBoard: A Neuromorphic Computing Platform for Hybrid Neural Networks in Robot Dog Application." Electronics 13, no. 18 (2024): 3619. http://dx.doi.org/10.3390/electronics13183619.

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Hybrid neural networks (HNNs), integrating the strengths of artificial neural networks (ANNs) and spiking neural networks (SNNs), provide a promising solution towards generic artificial intelligence. There is a prevailing trend towards designing unified SNN-ANN paradigm neuromorphic computing chips to support HNNs, but developing platforms to advance neuromorphic computing systems is equally essential. This paper presents the PAIBoard platform, which is designed to facilitate the implementation of HNNs. The platform comprises three main components: the upper computer, the communication module, and the neuromorphic computing chip. Both hardware and software performance measurements indicate that our platform achieves low power consumption, high energy efficiency and comparable task accuracy. Furthermore, PAIBoard is applied in a robot dog for tracking and obstacle avoidance system. The tracking module combines data from ultra-wide band (UWB) transceivers and vision, while the obstacle avoidance module utilizes depth information from an RGB-D camera, which further underscores the potential of our platform to tackle challenging tasks in real-world applications.

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Shen, Yuxiang. "Computer Vision: Technologies and Applications." Applied and Computational Engineering 163, no. 1 (2025): 35–41. https://doi.org/10.54254/2755-2721/2025.23817.

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Computer vision, as a crucial branch of artificial intelligence, is profoundly transforming various aspects of human society. This paper provides a systematic exploration of the key technologies, application domains, challenges, and future development trends in computer vision. We begin with a detailed analysis of core technologies including convolutional neural networks, Transformer architectures, and edge computing. Subsequently, we conduct an in-depth investigation of innovative applications in healthcare, autonomous driving, smart agriculture, and security surveillance. Furthermore, we examine critical challenges such as data scarcity, ethical privacy concerns, and computational energy consumption. Finally, we present future research directions including neuromorphic vision systems and quantum machine learning. By synthesizing insights from 15 authoritative references, this study aims to provide comprehensive technical references and application guidance for both academia and industry.

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Yang, Chaofei, Ximing Qiao, and Yiran Chen. "Neuromorphic Computing Systems: From CMOS To Emerging Nonvolatile Memory." IPSJ Transactions on System LSI Design Methodology 12 (2019): 53–64. http://dx.doi.org/10.2197/ipsjtsldm.12.53.

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Jeong, YeonJoo, Mohammed A. Zidan, and Wei D. Lu. "Parasitic Effect Analysis in Memristor-Array-Based Neuromorphic Systems." IEEE Transactions on Nanotechnology 17, no. 1 (2018): 184–93. http://dx.doi.org/10.1109/tnano.2017.2784364.

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Bhattacharya, Tinish, Sai Li, Yangqi Huang, Wang Kang, Weisheng Zhao, and Manan Suri. "Low-Power (1T1N) Skyrmionic Synapses for Spiking Neuromorphic Systems." IEEE Access 7 (2019): 5034–44. http://dx.doi.org/10.1109/access.2018.2886854.

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Gautam, Ashish, and Takashi Kohno. "Adaptive STDP Learning with Lateral Inhibition for Neuromorphic Systems." Proceedings of International Conference on Artificial Life and Robotics 28 (February 9, 2023): 289–92. http://dx.doi.org/10.5954/icarob.2023.os12-1.

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McOWAN, PETER W., CHRISTOPHER BENTON, JASON DALE, and ALAN JOHNSTON. "A MULTI-DIFFERENTIAL NEUROMORPHIC APPROACH TO MOTION DETECTION." International Journal of Neural Systems 09, no. 05 (1999): 429–34. http://dx.doi.org/10.1142/s0129065799000435.

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This paper presents a multi-differential neuromorphic approach to motion detection. The model is based evidence for a differential operators interpretation of the properties of the cortical motion pathway. We discuss how this strategy, which provides a robusl measure of speed for a range of types of image motion using a single compulational mechanism, forms a usful framework in which to develop future neuromorphic motion systems. We also discuss both our approaches to developing computational motion models, and constraints in the design strategy for transferring motion models to other domains of early visual processing.

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Bieszczad, Andrzej, and Bernard Pagurek. "Neurosolver: Neuromorphic general problem solver." Information Sciences 105, no. 1-4 (1998): 239–77. http://dx.doi.org/10.1016/s0020-0255(97)10027-5.

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Xiao, Chao, Yao Wang, Jihua Chen, and Lei Wang. "Topology-Aware Mapping of Spiking Neural Network to Neuromorphic Processor." Electronics 11, no. 18 (2022): 2867. http://dx.doi.org/10.3390/electronics11182867.

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Neuromorphic processors, the new generation of brain-inspired non-von Neumann computing systems, are developed to better support the execution of spiking neural networks (SNNs). The neuromorphic processor typically consists of multiple cores and adopts the Network-on-Chip (NoC) as the communication framework. However, an unoptimized mapping of SNNs onto the neuromorphic processor results in lots of spike messages on NoC, which increases the energy consumption and spike latency on NoC. Addressing this problem, we present a fast toolchain, NeuToMa, to map SNNs onto the neuromorphic processor. NeuToMa exploits the global topology of SNNs and uses the group optimization strategy to partition SNNs into multiple clusters, significantly reducing the NoC traffic. Then, NeuToMa dispatches the clusters to neuromorphic cores, minimizing the average hop of spike messages and balancing the NoC workload. The experimental results show that compared with the state-of-the-art technique, NeuToMa reduces the spike latency and energy consumption by up to 55% and 86%, respectively.

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Qi, Meng, Tianquan Fu, Huadong Yang, Ye Tao, Chunran Li, and Xiaoming Xiu. "Reliable analog resistive switching behaviors achieved using memristive devices in AlO _x /HfO _x bilayer structure for neuromorphic systems." Semiconductor Science and Technology 37, no. 3 (2022): 035018. http://dx.doi.org/10.1088/1361-6641/ac3cc7.

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Abstract Human brain synaptic memory simulation based on resistive random access memory (RRAM) has enormous potential to replace the traditional von Neumann digital computer thanks to several advantages, including its simple structure, its high-density integration, and its capabilities regarding information storage and neuromorphic computing. Herein, the reliable resistive switching (RS) behaviors of RRAM are demonstrated by engineering the AlO x /HfO x bilayer structure. This allows for uniform multibit information storage. Further, the analog switching behaviors are capable of imitating several synaptic learning functions, including learning experience behaviors, short-term plasticity, long-term plasticity transition, and spike-timing-dependent plasticity (STDP). In addition, the memristor based on STDP learning rules is implemented in image pattern recognition. These results may show the potential of HfO x -based memristors for future information storage and neuromorphic computing applications.

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Serrano-Gotarredona, T., T. Prodromakis, and B. Linares-Barranco. "A Proposal for Hybrid Memristor-CMOS Spiking Neuromorphic Learning Systems." IEEE Circuits and Systems Magazine 13, no. 2 (2013): 74–88. http://dx.doi.org/10.1109/mcas.2013.2256271.

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Kotov, V. B., and F. A. Yudkin. "Modeling and Characterization of Resistor Elements for Neuromorphic Systems." Optical Memory and Neural Networks 28, no. 4 (2019): 271–82. http://dx.doi.org/10.3103/s1060992x19040040.

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

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The concept of neuromorphic devices, aiming to process large amounts of information in parallel, at low power, high speed, and high efficiency, is to mimic the functions of human brain by emulating biological neural behavior. Optoelectronic neuromorphic devices are particularly suitable for neuromorphic applications with their ability to generate various pulses based on wavelength and to control synaptic stimulation. Each wavelength (ultraviolet, visible, and infrared) has specific advantages and optimal applications. Here, the heterostructure-based optoelectronic neuromorphic devices are 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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Mammadov, Elshen, Annagi Asgarov, and Aysen Mammadova. "The Role of Artificial Intelligence in Modern Computer Architecture: From Algorithms to Hardware Optimization." Porta Universorum 1, no. 2 (2025): 65–71. https://doi.org/10.69760/portuni.010208.

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The rapid advancement of artificial intelligence (AI) has significantly influenced the design and evolution of modern computer architectures. This article explores the dynamic relationship between AI algorithms and hardware, focusing on how neural networks have driven the development of specialized processors such as GPUs, TPUs, and neuromorphic chips. Through comparative analysis, performance benchmarking, and model-hardware interaction, the study highlights the transition from general-purpose computing systems to AI-optimized platforms. It also addresses emerging challenges related to scalability, energy efficiency, and security. The findings call for deeper interdisciplinary collaboration between AI researchers and hardware engineers to build systems that are both high-performing and sustainable in the age of intelligent computing.

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Yang, Zonglin, Liren Yang, Wendi Bao, et al. "High-Speed Object Recognition Based on a Neuromorphic System." Electronics 11, no. 24 (2022): 4179. http://dx.doi.org/10.3390/electronics11244179.

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Neuromorphic systems are bio-inspired and have the potential to break through the bottleneck of existing intelligent systems. This paper proposes a neuromorphic high-speed object recognition method based on DVS and SpiNNaker and implements a system in which an OR logic aggregation algorithm is used to acquire sufficient effective information and the asynchronous sparse computing mechanism of SNNs is exploited to reduce the computation. The experiment’s results show that the object detection rate of the designed system is more than 99% at the rotating speed of 900~2300 rpm; its response time is within 2.5 ms; and it requires 96.3% less computation than traditional recognition systems using the same scaled ANN.

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Khajooei Nejad, Arash, Mohammad (Behdad) Jamshidi, and Shahriar B. Shokouhi. "Implementing Tensor-Organized Memory for Message Retrieval Purposes in Neuromorphic Chips." Computers 12, no. 10 (2023): 189. http://dx.doi.org/10.3390/computers12100189.

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This paper introduces Tensor-Organized Memory (TOM), a novel neuromorphic architecture inspired by the human brain’s structural and functional principles. Utilizing spike-timing-dependent plasticity (STDP) and Hebbian rules, TOM exhibits cognitive behaviors similar to the human brain. Compared to conventional architectures using a simplified leaky integrate-and-fire (LIF) neuron model, TOM showcases robust performance, even in noisy conditions. TOM’s adaptability and unique organizational structure, rooted in the Columnar-Organized Memory (COM) framework, position it as a transformative digital memory processing solution. Innovative neural architecture, advanced recognition mechanisms, and integration of synaptic plasticity rules enhance TOM’s cognitive capabilities. We have compared the TOM architecture with a conventional floating-point architecture, using a simplified LIF neuron model. We also implemented tests with varying noise levels and partially erased messages to evaluate its robustness. Despite the slight degradation in performance with noisy messages beyond 30%, the TOM architecture exhibited appreciable performance under less-than-ideal conditions. This exploration into the TOM architecture reveals its potential as a framework for future neuromorphic systems. This study lays the groundwork for future applications in implementing neuromorphic chips for high-performance intelligent edge devices, thereby revolutionizing industries and enhancing user experiences within the power of artificial intelligence.

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Pavloski, Raymond. "Progress in Developing an Emulation of a Neuromorphic Device That Is Predicted to Enhance Existing Cortical Prosthetic Vision Technology by Engaging Desired Visual Geometries." Prosthesis 4, no. 4 (2022): 600–623. http://dx.doi.org/10.3390/prosthesis4040049.

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The utility of currently available cortical prosthetic vision systems is disappointing. The essential features of a neuromorphic device that is predicted to enhance vision provided by available systems follow from a hypothesis which states that the objective and subjective aspects of cortical prosthetic vision jointly constitute patterns that emerge from specified synaptic interactions. The research reported here completes several required steps in developing an emulation of this device: (1) replication of small-scale simulations that are consistent with the hypothesis using the NEST (Écublens, Vaud, Switzerland) simulator, which can also be used for full-scale network emulation by a neuromorphic computer; (2) testing whether results consistent with the hypothesis survive increasing the scale and duration of simulations; (3) establishing a method that uses numbers of spikes produced by network neurons to report the number of phosphenes produced by cortical stimulation; and (4) simulating essential functions of a neuromorphic device which is predicted to enhance current prosthetic systems. NEST simulations replicated early results and increasing their scale and duration produced results consistent with the hypothesis. A decision function created using multinomial logistic regression correctly reported the expected number of phosphenes for three sets of 2080 spike number distributions in which half of each set arises from simulations expected to yield continuous visual forms by engaging a desired visual geometry. A process for modulating electrical stimulation amplitude based on intermittent population recordings that is predicted to produce desired visual geometries was successfully simulated. Implications of these results for future research are discussed.

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Hazan, Avi, and Elishai Ezra Tsur. "Neuromorphic Neural Engineering Framework-Inspired Online Continuous Learning with Analog Circuitry." Applied Sciences 12, no. 9 (2022): 4528. http://dx.doi.org/10.3390/app12094528.

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Neuromorphic hardware designs realize neural principles in electronics to provide high-performing, energy-efficient frameworks for machine learning. Here, we propose a neuromorphic analog design for continuous real-time learning. Our hardware design realizes the underlying principles of the neural engineering framework (NEF). NEF brings forth a theoretical framework for the representation and transformation of mathematical constructs with spiking neurons, thus providing efficient means for neuromorphic machine learning and the design of intricate dynamical systems. Our analog circuit design implements the neuromorphic prescribed error sensitivity (PES) learning rule with OZ neurons. OZ is an analog implementation of a spiking neuron, which was shown to have complete correspondence with NEF across firing rates, encoding vectors, and intercepts. We demonstrate PES-based neuromorphic representation of mathematical constructs with varying neuron configurations, the transformation of mathematical constructs, and the construction of a dynamical system with the design of an inducible leaky oscillator. We further designed a circuit emulator, allowing the evaluation of our electrical designs on a large scale. We used the circuit emulator in conjunction with a robot simulator to demonstrate adaptive learning-based control of a robotic arm with six degrees of freedom.

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Chen, Guang, Hu Cao, Muhammad Aafaque, et al. "Neuromorphic Vision Based Multivehicle Detection and Tracking for Intelligent Transportation System." Journal of Advanced Transportation 2018 (December 2, 2018): 1–13. http://dx.doi.org/10.1155/2018/4815383.

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Neuromorphic vision sensor is a new passive sensing modality and a frameless sensor with a number of advantages over traditional cameras. Instead of wastefully sending entire images at fixed frame rate, neuromorphic vision sensor only transmits the local pixel-level changes caused by the movement in a scene at the time they occur. This results in advantageous characteristics, in terms of low energy consumption, high dynamic range, sparse event stream, and low response latency, which can be very useful in intelligent perception systems for modern intelligent transportation system (ITS) that requires efficient wireless data communication and low power embedded computing resources. In this paper, we propose the first neuromorphic vision based multivehicle detection and tracking system in ITS. The performance of the system is evaluated with a dataset recorded by a neuromorphic vision sensor mounted on a highway bridge. We performed a preliminary multivehicle tracking-by-clustering study using three classical clustering approaches and four tracking approaches. Our experiment results indicate that, by making full use of the low latency and sparse event stream, we could easily integrate an online tracking-by-clustering system running at a high frame rate, which far exceeds the real-time capabilities of traditional frame-based cameras. If the accuracy is prioritized, the tracking task can also be performed robustly at a relatively high rate with different combinations of algorithms. We also provide our dataset and evaluation approaches serving as the first neuromorphic benchmark in ITS and hopefully can motivate further research on neuromorphic vision sensors for ITS solutions.

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Yu, Cho, and Park. "A Silicon-Compatible Synaptic Transistor Capable of Multiple Synaptic Weights toward Energy-Efficient Neuromorphic Systems." Electronics 8, no. 10 (2019): 1102. http://dx.doi.org/10.3390/electronics8101102.

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In order to resolve the issue of tremendous energy consumption in conventional artificial intelligence, hardware-based neuromorphic system is being actively studied. Although various synaptic devices for the system have been proposed, they have shown limits in terms of endurance, reliability, energy efficiency, and Si processing compatibility. In this work, we design a synaptic transistor with short-term and long-term plasticity, high density, high reliability and energy efficiency, and Si processing compatibility. The synaptic characteristics of the device are closely examined and validated through technology computer-aided design (TCAD) device simulation. Consequently, full synaptic functions with high energy efficiency have been realized.

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Sánchez Quintana, Carlos, Francisco Moreno Arcas, David Albarracín Molina, José David Fernández Rodriguez, and Francisco J. Vico. "Melomics: A Case-Study of AI in Spain." AI Magazine 34, no. 3 (2013): 99–103. http://dx.doi.org/10.1609/aimag.v34i3.2464.

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Traditionally focused on good old-fashioned AI and robotics, the Spanish AI community holds a vigorous computational intelligence substrate. Neuromorphic, evolutionary, or fuzzylike systems have been developed by many research groups in the Spanish computer sciences. It is no surprise, then, that these naturegrounded efforts start to emerge, enriching the AI catalogue of research projects and publications and, eventually, leading to new directions of basic or applied research. In this article, we review the contribution of Melomics in computational creativity.

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

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Bessa, Wallace Moreira, and Gabriel da Silva Lima. "Intelligent Control of Seizure-Like Activity in a Memristive Neuromorphic Circuit Based on the Hodgkin–Huxley Model." Journal of Low Power Electronics and Applications 12, no. 4 (2022): 54. http://dx.doi.org/10.3390/jlpea12040054.

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Memristive neuromorphic systems represent one of the most promising technologies to overcome the current challenges faced by conventional computer systems. They have recently been proposed for a wide variety of applications, such as nonvolatile computer memory, neuroprosthetics, and brain–machine interfaces. However, due to their intrinsically nonlinear characteristics, they present a very complex dynamic behavior, including self-sustained oscillations, seizure-like events, and chaos, which may compromise their use in closed-loop systems. In this work, a novel intelligent controller is proposed to suppress seizure-like events in a memristive circuit based on the Hodgkin–Huxley equations. For this purpose, an adaptive neural network is adopted within a Lyapunov-based nonlinear control scheme to attenuate bursting dynamics in the circuit, while compensating for modeling uncertainties and external disturbances. The boundedness and convergence properties of the proposed control scheme are rigorously proved by means of a Lyapunov-like stability analysis. The obtained results confirm the effectiveness of the proposed intelligent controller, presenting a much improved performance when compared with a conventional nonlinear control scheme.

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