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Journal articles on the topic 'Hardware for Artificial Intelligence'

Author: Grafiati

Published: 15 June 2024

Last updated: 31 July 2025

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1

Burkert, Andreas. "Hardware for Artificial Intelligence." ATZ worldwide 121, no. 5 (2019): 8–13. http://dx.doi.org/10.1007/s38311-019-0060-0.

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Burkert, Andreas. "Hardware for Artificial Intelligence." ATZelectronics worldwide 14, no. 3 (2019): 8–13. http://dx.doi.org/10.1007/s38314-019-0026-4.

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3

Popov, I. "SoC hardware supporting artificial intelligence." ELECTRONICS: Science, Technology, Business, no. 7 (2018): 116–23. http://dx.doi.org/10.22184/1992-4178.2018.178.7.116.123.

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Albin, Vincent Vettukattil. "Artificial Intelligence." Vidyankur Journal of Philosophical & Theological Studies XXV, February 2023, no. 1 (2023): 50–58. https://doi.org/10.5281/zenodo.7642777.

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In the contemporary scenario, technology has become a part and parcel of our life. In fact, the aim of technology is to help humans transcend their limitations. Artificial intelligence (AI) and related hardware is one such technology that is bringing about revolutionary changes in the various domains of human life.

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VerWey, John. "The Other Artificial Intelligence Hardware Problem." Computer 55, no. 1 (2022): 34–42. http://dx.doi.org/10.1109/mc.2021.3113271.

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Johnson, Jeffrey, Peter Denning, Andrew Odlyzko, et al. "Artificial Intelligence: Foundational Technologies of Artificial Intelligence." Ubiquity 2025, July (2025): 1–10. https://doi.org/10.1145/3747355.

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More than the 70 years since its emergence in the early 1950s, artificial intelligence (AI) is performing cognitive tasks traditionally considered the unique province of humans. This progress did not occur in a vacuum. AI emerged against a rich background of technologies from computer science and ideas about intelligence and learning from philosophy, psychology, logic, game theory, and cognitive science. We sketch out the enabling technologies for AI. They include search, reasoning, neural networks, natural language processing, signal processing and computer graphics, programming and conventio

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Raj, Anish. "Artificial Intelligence." International Journal for Research in Applied Science and Engineering Technology 12, no. 11 (2024): 646–55. http://dx.doi.org/10.22214/ijraset.2024.64695.

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Artificial intelligence is a science that aims to perform tasks that require human intelligence. For the past two years, it has been used as a development tool in many areas such as prediction, health, security, and also improves the performance of production and services. Since artificial intelligence and its operations are based on too much data, algorithms, and scientific data, users cannot understand and comprehend the content and do not have the skills necessary to use this technology. Since artificial intelligence is controlled by machines and algorithms, it is difficult to determine the

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Prati, Enrico. "Quantum neuromorphic hardware for quantum artificial intelligence." Journal of Physics: Conference Series 880 (August 2017): 012018. http://dx.doi.org/10.1088/1742-6596/880/1/012018.

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李, 肖. "Artificial Intelligence Driven Hardware Trojan Implantation Detection." Computer Science and Application 15, no. 06 (2025): 120–27. https://doi.org/10.12677/csa.2025.156163.

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Kintonova, A. Zh, B. B. Suleimenova, and A. K. Shangytbayeva. "ARTIFICIAL INTELLIGENCE IN EDUCATION." Yessenov Science Journal 48, no. 3 (2024): 303–9. http://dx.doi.org/10.56525/uwkm4465.

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The article is devoted to the problem of using robots in the educational process in the training of specialists. The article describes training robots based on the Robot Operating System, i.e. based on a set of software libraries and tools that help create applications for robots. Robot Integration with ROS (Robot Operating System) is the process of combining the hardware and software components of a robot with the ROS infrastructure. This allows the robot to interact with other devices and systems, as well as use a variety of ready-made tools and libraries available within the ecosystem. Robo

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Yoon, Young Hyun, Dong Hyun Hwang, Jun Hyeok Yang, and Seung Eun Lee. "Intellino: Processor for Embedded Artificial Intelligence." Electronics 9, no. 7 (2020): 1169. http://dx.doi.org/10.3390/electronics9071169.

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The development of computation technology and artificial intelligence (AI) field brings about AI to be applied to various system. In addition, the research on hardware-based AI processors leads to the minimization of AI devices. By adapting the AI device to the edge of internet of things (IoT), the system can perform AI operation promptly on the edge and reduce the workload of the system core. As the edge is influenced by the characteristics of the embedded system, implementing hardware which operates with low power in restricted resources on a processor is necessary. In this paper, we propose

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Wang, Xiaoyin. "Artificial intelligence enhanced environmental detection system." Applied and Computational Engineering 66, no. 1 (2024): 156–59. http://dx.doi.org/10.54254/2755-2721/66/20240938.

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This paper presents a novel approach to improve the accuracy of environmental detection and prediction by incorporating artificial intelligence (AI) technology into existing detection systems. At the heart of our approach lies the combination of a complex AI model with the hardware and software components of the inspection system. This combined approach can significantly improve the accuracy of detection systems through greater ability to predict environmental changes and events, underscoring the superior performance of hardware and software combined with AI technology. This paper delves into

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Wang, Xiaoyin. "Hardware Failure Prediction Modes Powered by Artificial Intelligence." Academic Journal of Science and Technology 12, no. 3 (2024): 199–201. http://dx.doi.org/10.54097/5qrhwj73.

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With the increase of the system scale and complexity of modern computer systems and electronic equipment, the physical components (hardware) in computer or electronic equipment are damaged, the performance is reduced or the normal operation of computer and electronic systems is greatly increased due to various reasons. Detecting and predicting hardware faults in time is the key to ensure system stability and reliability. Hardware failure prediction technology based on artificial intelligence (AI) can effectively predict potential hardware failures by analyzing historical data, monitoring equip

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HNATCHUK, YELYZAVETA, YEVHENIY SIERHIEIEV, and ALINA HNATCHUK. "USING ARTIFICIAL INTELLIGENCE ACCELERATORS TO TRAIN COMPUTER GAME CHARACTERS." Computer systems and information technologies, no. 1 (August 21, 2021): 63–70. http://dx.doi.org/10.31891/csit-2021-3-9.

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A review of the literature has shown that today, given the complexity of computational processes and the high cost of these processes, the gaming computer industry needs to improve hardware and software to increase the efficiency and speed of processing artificial intelligence algorithms. An analysis of existing machine learning tools and existing hardware solutions to accelerate artificial intelligence. A reasonable choice of hardware solutions that are most effective for the implementation of the task. Possibilities of practical use of the artificial intelligence accelerator are investigated

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Rossi, Dr Elena, and Dr Samuel O. Mensah. "Brain-Inspired Computing: Bridging Neurobiology and Artificial Intelligence." International Research Journal of Advanced Engineering and Technology 2, no. 6 (2025): 1–4. https://doi.org/10.55640/irjaet-v02i06-01.

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This paper explores brain-inspired computing as a transformative approach that integrates principles from neurobiology with artificial intelligence (AI) to enhance computational efficiency and adaptability. By mimicking neural structures and cognitive processes, brain-inspired models aim to overcome limitations of traditional AI systems, enabling more robust learning, pattern recognition, and decision-making. The study reviews key neurobiological mechanisms, such as neural plasticity and parallel processing, and discusses their applications in neuromorphic hardware and advanced AI algorithms.

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Chen, Kevin P. "Artificial Intelligence-based Musical Instrument Accompaniment System." Applied and Computational Engineering 2, no. 1 (2023): 679–84. http://dx.doi.org/10.54254/2755-2721/2/20220645.

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The string learning process requires students to be able to accurately grasp the accuracy of notes and rhythms in the process of playing, and its evaluation is usually done by instructors, but long one-on-one instruction is difficult to achieve in actual teaching. In this project, through the research of audio hardware systems and digital signal processing software technology, I design a string performance recognition robot system combining hardware and software, to realize automatic accompanying practice. The hardware of this system consists of a Raspberry Pi card-type computer, recording equ

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Smith, Adam Leon. "Artificial Intelligence." ITNOW 64, no. 3 (2022): 47. http://dx.doi.org/10.1093/combul/bwac093.

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Smith, Adam Leon. "Artificial Intelligence." ITNOW 64, no. 2 (2022): 65. http://dx.doi.org/10.1093/itnow/bwac065.

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Smith, Adam Leon. "Artificial Intelligence." ITNOW 64, no. 1 (2022): 41. http://dx.doi.org/10.1093/itnow/bwac021.

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Smith, Adam Leon. "Artificial Intelligence." ITNOW 65, no. 1 (2023): 57. http://dx.doi.org/10.1093/combul/bwad031.

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Ratnayake, Deepthi. "Artificial Intelligence." ITNOW 65, no. 4 (2023): 43. http://dx.doi.org/10.1093/itnow/bwad128.

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Abstract Dr Deepthi Ratnayake, Principal Lecturer in Computer Science at University of Hertfordshire, and L. Fox Thomas MBCS, Vice Chair of BCS ISSG explore AI and its impact on women's safety and security.

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Thakare, Pratik Manoj. "Bridging the Gap Between Quantum Computing and Artificial Intelligence." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 12 (2023): 1–10. http://dx.doi.org/10.55041/ijsrem27848.

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Quantum machine learning (QML) holds the potential to transform various industries, yet its widespread adoption faces formidable challenges. This paper provides a condensed exploration of these challenges and opportunities. I delve into: • Hardware Limitations: Present quantum computers are constrained in qubit count and gate quality, posing obstacles to real-world QML implementation. We dissect the implications of these limitations on QML computations. • Error Correction: Quantum systems are prone to errors stemming from hardware noise and gate imperfections. We scrutinize the strategies to m

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Chen, Hongxi. "Hardware Implementation for Convolutional Neural Networks in Artificial Intelligence." Highlights in Science, Engineering and Technology 62 (July 27, 2023): 73–77. http://dx.doi.org/10.54097/hset.v62i.10426.

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Artificial Intelligence (AI) has brought great convenience and help to human society by improving efficiency, increasing productivity and reducing cost. As a part of deep learning, convolutional neural networks (CNNs) have been widely concerned by researchers in recent years. In this paper, five parts of the CNN structure, including input layer, convolutional layer, pooling layer, fully connected layer, activation function and output layer are going to be elaborated. Besides, four different kinds of hardware, which can be used in AI implementation, including graphics processing unit (GPU), fie

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Gigan, Sylvain, Florent Krzakala, Laurent Daudet, and Igor Carron. "Artificial intelligence: From electronics to optics." Photoniques, no. 104 (September 2020): 49–52. http://dx.doi.org/10.1051/photon/202010449.

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Machine Learning and big data are currently revolutionizing our way of life, in particular with the recent emergence of deep learning. Powered by CPU and GPU, they are currently hardware limited and extremely energy intensive. Photonics, either integrated or in free space, offers a very promising alternative for realizing optically machine learning tasks at high speed and low consumption. We here review the history and current state of the art of optical computing and optical machine learning.

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Liakos, Konstantinos G., Georgios K. Georgakilas, Fotis C. Plessas, and Paris Kitsos. "GAINESIS: Generative Artificial Intelligence NEtlists SynthesIS." Electronics 11, no. 2 (2022): 245. http://dx.doi.org/10.3390/electronics11020245.

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A significant problem in the field of hardware security consists of hardware trojan (HT) viruses. The insertion of HTs into a circuit can be applied for each phase of the circuit chain of production. HTs degrade the infected circuit, destroy it or leak encrypted data. Nowadays, efforts are being made to address HTs through machine learning (ML) techniques, mainly for the gate-level netlist (GLN) phase, but there are some restrictions. Specifically, the number and variety of normal and infected circuits that exist through the free public libraries, such as Trust-HUB, are based on the few sample

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Hong, Seong Hyeon, and Yi Wang. "Artificial Intelligence and Its Applications in Robotics." Applied Sciences 15, no. 14 (2025): 7907. https://doi.org/10.3390/app15147907.

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Prior to the advent of intelligent robots, artificial intelligence (AI) and robotics were considered two distinct fields; however, the rapid development of hardware with higher computational power and more precise sensors, along with the exponential increase in data, has contributed to significant advancements in the integration of the two fields [...]

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Chen, Zhimei. "Hardware Accelerated Optimization of Deep Learning Model on Artificial Intelligence Chip." Frontiers in Computing and Intelligent Systems 6, no. 2 (2023): 11–14. http://dx.doi.org/10.54097/fcis.v6i2.03.

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With the rapid development of deep learning technology, the demand for computing resources is increasing, and the accelerated optimization of hardware on artificial intelligence (AI) chip has become one of the key ways to solve this challenge. This paper aims to explore the hardware acceleration optimization strategy of deep learning model on AI chip to improve the training and inference performance of the model. In this paper, the method and practice of optimizing deep learning model on AI chip are deeply analyzed by comprehensively considering the hardware characteristics such as parallel pr

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Tan, K. C., L. F. Wang, T. H. Lee, and P. Vadakkepat. "Evolvable Hardware in Evolutionary Robotics." Autonomous Robots 16, no. 1 (2004): 5–21. http://dx.doi.org/10.1023/b:auro.0000008669.57012.88.

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Boya Marqas, Ridwan, Saman M. Almufty, Prof Dr ENGİN AVCI, and Renas R. Asaad. "Optimizing Artificial Intelligence Systems for Real-World Applications." International Journal of Scientific World 11, no. 1 (2025): 40–47. https://doi.org/10.14419/xxc0jx38.

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The optimization of Artificial Intelligence (AI) systems is critical for improving performance, scalability, and adaptability across various real-world applications. This paper explores key optimization techniques, including algorithmic enhancements, hardware acceleration, software tools, and data preprocessing. Challenges such as resource constraints, domain-specific requirements, and ethical concerns are analyzed. Case studies in healthcare, finance, manufacturing, and autonomous systems demonstrate notable improvements in accuracy, efficiency, and scalability. A systematic framework is prop

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Barrutia, Richard. "COMMUNICATIVE CALL WITH ARTIFICIAL INTELLIGENCE: SOME DESIDERATA." CALICO Journal 3, no. 1 (2013): 37–42. http://dx.doi.org/10.1558/cj.v3i1.37-42.

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The ideal CALL courseware development procedure will take advantage of many or all of the latest hardware, pedagogical, and theoretical advances in the language teaching field. These advances are presented in this paper both as a description o the ideal CALL courseware and, where applicable, as actual design implementations in a Spanish course being developed by the author.

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R. Otero, Angel, and Keiron Hylton. "Exploring the Integration of Artificial Intelligence into the Functions of an Accounting Department." International Journal of Artificial Intelligence & Applications 16, no. 1 (2025): 33–45. https://doi.org/10.5121/ijaia.2025.16103.

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Artificial intelligence is transforming various fields, including accounting, by representing a significant technological innovation. Artificial intelligence combines hardware and software to simulate human cognitive processes, enabling machines to perform complex tasks such as learning, reasoning, and decision-making. This paper explores the advantages and disadvantages of integrating artificial intelligence into accounting practices. While artificial intelligence presents numerous benefits for accountants, it also introduces challenges that must be addressed. The paper also contributes to th

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AIRCC. "Exploring the Integration of Artificial Intelligence into the Functions of an Accounting Department." International Journal of Artificial Intelligence & Applications (IJAIA) 16, no. 1 (2025): 33–45. https://doi.org/10.5121/ijaia.2025.16103.

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Artificial intelligence is transforming various fields, including accounting, by representing a significant technological innovation. Artificial intelligence combines hardware and software to simulate human cognitive processes, enabling machines to perform complex tasks such as learning, reasoning, and decision-making. This paper explores the advantages and disadvantages of integrating artificial intelligence into accounting practices. While artificial intelligence presents numerous benefits for accountants, it also introduces challenges that must be addressed. The paper also contributes to th

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

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

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Pilare, Piyush, Coral Mahato, Chanchal Khergade, Shubham Agrawal, and Prasheel Thakre. "Implementation of Hand Gesture-Controlled Mouse Using Artificial Intelligence." 3C Tecnología_Glosas de innovación aplicadas a la pyme 11, no. 2 (2022): 71–79. http://dx.doi.org/10.17993/3ctecno.2022.v11n2e42.71-79.

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This article presents a proposed mouse system. In this paper, we discussed the implementation of an artificially intelligent hand gesture-controlled mouse that uses computer vision to execute mouse functions using the Colour detection technique. The virtual mouse uses the current python and computer vision algorithms for the recognition of the Masked/colored region and works seamlessly without any extra hardware requirements. A computer may be controlled remotely using hand motions, and it is capable to perform cursor movement, left-clicking, and right-clicking without the need for a hardware

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Siau, Keng, and Weiyu Wang. "Artificial Intelligence (AI) Ethics." Journal of Database Management 31, no. 2 (2020): 74–87. http://dx.doi.org/10.4018/jdm.2020040105.

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Artificial intelligence (AI)-based technology has achieved many great things, such as facial recognition, medical diagnosis, and self-driving cars. AI promises enormous benefits for economic growth, social development, as well as human well-being and safety improvement. However, the low-level of explainability, data biases, data security, data privacy, and ethical problems of AI-based technology pose significant risks for users, developers, humanity, and societies. As AI advances, one critical issue is how to address the ethical and moral challenges associated with AI. Even though the concept

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Volnov, Nikita A. "THE USE OF ARTIFICIAL INTELLIGENCE TO ASSESS INDUSTRY AND REGIONAL REAL ESTATE AS A MODERN TOOL FOR INNOVATIVE DEVELOPMENT OF TERRITORIES." EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA 2/12, no. 155 (2025): 61–72. https://doi.org/10.36871/ek.up.p.r.2025.02.12.010.

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Artificial intelligence has been the focus of research for a long time. During this time, serious progress has been achieved: more powerful hardware and software have been developed, computing power and data transmission have been improved. The peculiarity of artificial intelligence is that by studying and analyzing the source data, intelligence improves. The use of artificial intelligence creates completely new opportunities for flexible and efficient production, even when it comes to complex technological processes. Data managers of manufacturing companies use artificial intelligence to iden

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Wang, Xibei, Shizhen Weng, and Ziyu Xie. "Artificial intelligence in clinical applications." Applied and Computational Engineering 48, no. 1 (2024): 96–105. http://dx.doi.org/10.54254/2755-2721/48/20241199.

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Modern medicine has improved to the point that intelligent diagnostic tools and auxiliary medical technology, such as surgical robots and image analysis systems, are now widespread in clinical settings. In clinical practice, the performance of different surgical robots and image analysis systems is very different, which seriously limits the use of complex medical scenes. The algorithm models and robotic arms that these intelligent robots and the supporting systems rely on have being recognized by the researchers. In this study, hardware and software algorithms are introduced one at a time, wit

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Shen, Yingying. "Artificial Intelligence and the Economy - The Impact of Artificial Intelligence on the Job Market." Advances in Economics, Management and Political Sciences 92, no. 1 (2024): 71–74. http://dx.doi.org/10.54254/2754-1169/92/20231275.

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Because of the improvement of the performance of modern computer hardware and the continuous development of algorithms, the application of artificial intelligence is more widely used in all walks of life. In this work, the application of artificial intelligence technology in the fields of finance, medical care, industry, information, education and social life, especially in the manufacturing industry, has formed an unstoppable trend. For future careers, the arrival of artificial intelligence is also thought-provoking. In addition to bringing a lot of new jobs, but also let some low-cost, labor

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Hwang, Dong Hyun, Chang Yeop Han, Hyun Woo Oh, and Seung Eun Lee. "ASimOV: A Framework for Simulation and Optimization of an Embedded AI Accelerator." Micromachines 12, no. 7 (2021): 838. http://dx.doi.org/10.3390/mi12070838.

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Artificial intelligence algorithms need an external computing device such as a graphics processing unit (GPU) due to computational complexity. For running artificial intelligence algorithms in an embedded device, many studies proposed light-weighted artificial intelligence algorithms and artificial intelligence accelerators. In this paper, we propose the ASimOV framework, which optimizes artificial intelligence algorithms and generates Verilog hardware description language (HDL) code for executing intelligence algorithms in field programmable gate array (FPGA). To verify ASimOV, we explore the

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Widdows, Dominic, Kirsty Kitto, and Trevor Cohen. "Quantum Mathematics in Artificial Intelligence." Journal of Artificial Intelligence Research 72 (December 14, 2021): 1307–41. http://dx.doi.org/10.1613/jair.1.12702.

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In the decade since 2010, successes in artificial intelligence have been at the forefront of computer science and technology, and vector space models have solidified a position at the forefront of artificial intelligence. At the same time, quantum computers have become much more powerful, and announcements of major advances are frequently in the news. The mathematical techniques underlying both these areas have more in common than is sometimes realized. Vector spaces took a position at the axiomatic heart of quantum mechanics in the 1930s, and this adoption was a key motivation for the d

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Dubljanin, Dobrivoje, Filip Marković, Gabriela Dimić, Dragan Vučković, Martina Petković, and Lazar Mosurović. "Educational Application of Artificial Intelligence for Diagnosing the State of Railway Tracks." International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE) 12, no. 2 (2024): 467–76. http://dx.doi.org/10.23947/2334-8496-2024-12-2-467-476.

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The aim of the work is to present an innovative solution based on artificial intelligence for examining the condition of railway tracks in real time. The system, based on fuzzy logic and metaheuristics such as Fuzzy Logic, Neural Networks and Bee Behavior Optimization, combines hardware and software to provide reliable data on the technical characteristics of the railway. Installed in rail vehicles, hardware collects this data, while software uses artificial intelligence to improve operational reliability and safety. The aforementioned technology is not only useful for infrastructure diagnosti

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Cao, Yuan, Zhi Han, Rui Kong, Canlin Zhang, and Qiu Xie. "Technical Composition and Creation of Interactive Installation Art Works under the Background of Artificial Intelligence." Mathematical Problems in Engineering 2021 (September 25, 2021): 1–11. http://dx.doi.org/10.1155/2021/7227416.

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Interactive installation art is a kind of art that uses specific software and computer hardware as a platform, a platform for interaction between humans and machines or different people through computer hardware. It is an interactive art that uses material installations in nature as a medium. Traditional interactive installation art is not safe and convenient, in order to solve the shortcomings of traditional interactive installation art. This article introduces artificial intelligence technology by studying the overview, development, and application of artificial intelligence. The encryption

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Li, Yingbo, Zhao Li, Yucong Duan, and Anamaria-Beatrice Spulber. "Physical artificial intelligence (PAI): the next-generation artificial intelligence." Frontiers of Information Technology & Electronic Engineering 24, no. 8 (2023): 1231–38. http://dx.doi.org/10.1631/fitee.2200675.

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Reinhardt, Sophia, Joshua Schmidt, Jonas Schneider, et al. "Smartphone-Based Videonystagmography Using Artificial Intelligence." Current Directions in Biomedical Engineering 9, no. 1 (2023): 528–31. http://dx.doi.org/10.1515/cdbme-2023-1132.

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Abstract Dizziness is a common symptom in medicine. The anamnesis and detection of a nystagmus is essential to distinguish a vertigo's pathogenesis. The diagnosis is complex, expensive, and not always available across the board. We present a novel location- and time-independent mobile application for videonystagmography (VNG) to support vertigo patients and medical staff. No additional hardware is necessary. The app uses artificial intelligence for eye tracking and to detect a horizontal nystagmus. A feasibility study of the mobile VNG with 13 healthy volunteers was performed. Each participant

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Smetanina, O. N., E. Yu Sazonova, and D. Yu Andrushko. "SOFTWARE AND HARDWARE COMPLEX FOR ASSESSING RELIABILITY USING ARTIFICIAL INTELLIGENCE." Современные наукоемкие технологии (Modern High Technologies), no. 7 2020 (2020): 90–97. http://dx.doi.org/10.17513/snt.38140.

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Hersam, Mark C. "(Invited) Enabling Bio-Realistic Artificial Intelligence Hardware with Neuromorphic Nanoelectronics." ECS Meeting Abstracts MA2024-01, no. 57 (2024): 3012. http://dx.doi.org/10.1149/ma2024-01573012mtgabs.

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The exponentially improving performance of digital computers has recently slowed due to the speed and power consumption issues resulting from the von Neumann bottleneck. In contrast, neuromorphic computing aims to circumvent these limitations by spatially co-locating logic and memory in a manner analogous to biological neuronal networks [1]. Beyond reducing power consumption, neuromorphic devices provide efficient architectures for image recognition, machine learning, and artificial intelligence [2]. This talk will explore how low-dimensional nanoelectronic materials enable gate-tunable neurom

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Su, Fei, Chunsheng Liu, and Haralampos-G. Stratigopoulos. "Special Issue on Testability and Dependability of Artificial Intelligence Hardware." IEEE Design & Test 40, no. 2 (2023): 5–7. http://dx.doi.org/10.1109/mdat.2023.3241114.

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Pande, Partha Pratim. "Special Issue on Testability and Dependability of Artificial Intelligence Hardware." IEEE Design & Test 40, no. 2 (2023): 4. http://dx.doi.org/10.1109/mdat.2023.3243862.

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Tursyn, M. S. "ROLE OF ARTIFICIAL INTELLIGENCE IN SIGN LANGUAGE RECOGNITION." Herald of the Kazakh-British technical university 22, no. 1 (2025): 94–102. https://doi.org/10.55452/1998-6688-2025-22-1-94-102.

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Over the decades the increasing computational capability and development of new technologies in the field of artificial intelligence have given us the ability to translate sign language in real time. There exist two main approaches to sign language recognition, the hardware-based approach and the software-based approach. The hardware-based approach relies on using special gloves, Kinect-based devices, and different levels of sensors. On the other hand, one of the approaches to working with sign language is using neural networks, which is the softwarebased approach. In this work, I observed exi

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Yuan, Yumin, and Xiyuan Li. "Application Strategies of Artificial Intelligence and Big Data Technology in Computer Monitoring and Control." Journal of Electronic Research and Application 9, no. 2 (2025): 29–34. https://doi.org/10.26689/jera.v9i2.9916.

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This article focuses on the current computer monitoring and control as the research direction, studying the application strategies of artificial intelligence and big data technology in this field. It includes an introduction to artificial intelligence and big data technology, the application strategies of artificial intelligence and big data technology in computer hardware, software, and network monitoring, as well as the application strategies of artificial intelligence and big data technology in computer process, access, and network control. This analysis aims to serve as a reference for the

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