Relevant bibliographies by topics / Hardware for Artificial Intelligence

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Hardware for Artificial Intelligence'

Author: Grafiati
Published: 15 June 2024
Last updated: 31 July 2025

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Journal articles on the topic "Hardware for Artificial Intelligence"

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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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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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Dissertations / Theses on the topic "Hardware for Artificial Intelligence"

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Orozco, Gabriel Mario. "Artificial intelligence opportunities and an end-do-end data-driven solution for predicting hardware failures." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/104304.

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Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2016. In conjunction with the Leaders for Global Operations Program at MIT.<br>Thesis: S.M. in Engineering Systems, Massachusetts Institute of Technology, Department of Mechanical Engineering, 2016. In conjunction with the Leaders for Global Operations Program at MIT.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 93-96).<br>Dell's target to provide quality products based on reliability, security, and manageability, has driven Dell Inc. to become one of the largest PC supp
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Cheng, Chih Kang. "Hardware implementation of the complex Hopfield neural network." CSUSB ScholarWorks, 1995. https://scholarworks.lib.csusb.edu/etd-project/1016.

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GRIMALDI, MATTEO. "Hardware-Aware Compression Techniques for Embedded Deep Neural Networks." Doctoral thesis, Politecnico di Torino, 2021. http://hdl.handle.net/11583/2933756.

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Bedi, Abhishek. "A generic platform for the evolution of hardware." Click here to access this resource online, 2009. http://hdl.handle.net/10292/651.

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Evolvable Hardware is a technique derived from evolutionary computation applied to a hardware design. The term evolutionary computation involves similar steps as involved in the human evolution. It has been given names in accordance with the electronic technology like, Genetic Algorithm (GA), Evolutionary Strategy (ES) and Genetic Programming (GP). In evolutionary computing, a configured bit is considered as a human chromosome for a genetic algorithm, which has to be downloaded into hardware. Early evolvable hardware experiments were conducted in simulation and the only elite chromosome was do
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MARRONE, FRANCESCO. "Memristor-based hardware accelerators: from device modeling to AI applications." Doctoral thesis, Politecnico di Torino, 2022. http://hdl.handle.net/11583/2972305.

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Al, Rawashdeh Khaled. "Toward a Hardware-assisted Online Intrusion Detection System Based on Deep Learning Algorithms for Resource-Limited Embedded Systems." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1535464571843315.

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Kumar, Sharad Kumar. "Analysis of Machine Learning Modeling Attacks on Ring Oscillator based Hardware Security." University of Toledo / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1541759752027838.

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CONTI, DANIELE. "Neuromorphic systems based on memristive devices - From the material science perspective to bio-inspired learning hardware." Doctoral thesis, Politecnico di Torino, 2018. http://hdl.handle.net/11583/2711511.

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Hardware computation is facing in the present age a deep transformation of its own paradigms. Silicon based computation is reaching its limit due to the physical constraints of transistor technology. As predicted by the Moore’s law, downscaling of transistor dimensions doubled each year since the 60s, leading nowadays to the extreme of 16-nm channel width of the present state-of-the-art technology. No further improvement is possible, since laws of physics impose a different electrical behavior when lower dimensions are attempted. Multiple solutions are then envisaged, spanning the range from
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Imbulgoda, Liyangahawatte Gihan Janith Mendis. "Hardware Implementation and Applications of Deep Belief Networks." University of Akron / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=akron1476707730643462.

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Brink, Stephen Isaac. "Learning in silicon: a floating-gate based, biophysically inspired, neuromorphic hardware system with synaptic plasticity." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/50143.

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The goal of neuromorphic engineering is to create electronic systems that model the behavior of biological neural systems. Neuromorphic systems can leverage a combination of analog and digital circuit design techniques to enable computational modeling, with orders of magnitude of reduction in size, weight, and power consumption compared to the traditional modeling approach based upon numerical integration. These benefits of neuromorphic modeling have the potential to facilitate neural modeling in resource-constrained research environments. Moreover, they will make it practical to use neural co
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Books on the topic "Hardware for Artificial Intelligence"

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Mishra, Ashutosh, Jaekwang Cha, Hyunbin Park, and Shiho Kim, eds. Artificial Intelligence and Hardware Accelerators. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-22170-5.

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Kropf, Thomas. Introduction to Formal Hardware Verification. Springer Berlin Heidelberg, 1999.

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Jovanović, Aleksandar S. Expert Systems in Structural Safety Assessment: Proceedings of an International Course October 2-4, 1989, Stuttgart, FRG. Springer Berlin Heidelberg, 1989.

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Lee, Bang W. Hardware annealing in analog VLSI neurocomputing. Kluwer Academic Publishers, 1991.

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Strous, Leon. Internet of Things. Information Processing in an Increasingly Connected World: First IFIP International Cross-Domain Conference, IFIPIoT 2018, Held at the 24th IFIP World Computer Congress, WCC 2018, Poznan, Poland, September 18-19, 2018, Revised Selected Papers. Springer Nature, 2019.

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Sood, A. K. Active Perception and Robot Vision. Springer Berlin Heidelberg, 1992.

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Jain, Anil K. Real-Time Object Measurement and Classification. Springer Berlin Heidelberg, 1988.

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Matthias, Scheutz, ed. Computationalism: New directions. MIT Press, 2002.

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Harris, Michael C. Artificial intelligence. Marshall Cavendish Benchmark, 2011.

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Karam, P. Andrew. Artificial intelligence. Chelsea House, 2011.

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Book chapters on the topic "Hardware for Artificial Intelligence"

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Chen, Robert H., and Chelsea Chen. "Computing Hardware." In Artificial Intelligence, 2nd ed. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003463542-1.

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Mishra, Ashutosh, Pamul Yadav, and Shiho Kim. "Artificial Intelligence Accelerators." In Artificial Intelligence and Hardware Accelerators. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-22170-5_1.

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Azoff, Eitan Michael. "AI hardware." In Toward Human-Level Artificial Intelligence. CRC Press, 2024. http://dx.doi.org/10.1201/9781003507864-12.

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Ahsan, S. M. Mojahidul, Tamzidul Hoque, Md Sakib Hasan, Mrittika Chowdhury, and Anurag Dhungel. "Hardware Accelerators for Artificial Intelligence." In AI-Enabled Electronic Circuit and System Design. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-71436-8_14.

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Yadav, Pamul, Ashutosh Mishra, and Shiho Kim. "Neuromorphic Hardware Accelerators." In Artificial Intelligence and Hardware Accelerators. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-22170-5_8.

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Lippmann, Bernhard, Matthias Ludwig, and Horst Gieser. "Generating Trust in Hardware through Physical Inspection." In Embedded Artificial Intelligence. River Publishers, 2023. http://dx.doi.org/10.1201/9781003394440-5.

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Liu, Yanli, Bochen Guan, Weiyi Li, Qinwen Xu, and Shuxue Quan. "SMOF: Squeezing More Out of Filters Yields Hardware-Friendly CNN Pruning." In Artificial Intelligence. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20497-5_20.

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Burns, Jeff. "The New Era of AI Hardware." In From Artificial Intelligence to Brain Intelligence. River Publishers, 2022. http://dx.doi.org/10.1201/9781003338215-4.

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Jhung, Junekyo, Ho Suk, Hyungbin Park, and Shiho Kim. "Hardware Accelerators for Autonomous Vehicles." In Artificial Intelligence and Hardware Accelerators. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-22170-5_9.

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Kim, Jinhyuk, and Shiho Kim. "Hardware Accelerators in Embedded Systems." In Artificial Intelligence and Hardware Accelerators. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-22170-5_6.

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Conference papers on the topic "Hardware for Artificial Intelligence"

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Kostadinov, Atanas N. "Application of Artificial Intelligence in Hardware Description Languages Education." In 2024 XXXIII International Scientific Conference Electronics (ET). IEEE, 2024. http://dx.doi.org/10.1109/et63133.2024.10721535.

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Garate, Eider, Paul San Sebastian, Guillermo Valverde, Alejandra Ruiz, and Meritxell Gómez. "Variational Quantum Regression on NISQ Hardware with Error Mitigation." In 2024 Artificial Intelligence Revolutions (AIR). IEEE, 2024. https://doi.org/10.1109/air63653.2024.00016.

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Sandeep, Dannapaneni, Abhishek Sharma, Mukesh Maurya, Vishnu Vardhan Chary, and Gururaj M. V. "Boosting Solar Load Forecasting Using Artificial Intelligence and Hardware Prototype." In 2024 IEEE 4th International Conference on Sustainable Energy and Future Electric Transportation (SEFET). IEEE, 2024. http://dx.doi.org/10.1109/sefet61574.2024.10718115.

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Gül, Fatih. "Memristive Synapses as Building Blocks of Neuromorphic Artificial Intelligence (AI) Hardware." In 2024 8th International Artificial Intelligence and Data Processing Symposium (IDAP). IEEE, 2024. http://dx.doi.org/10.1109/idap64064.2024.10711094.

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Mahfuz, Tanzim, Pravin Gaikwad, Tasneem Suha, Swarup Bhunia, and Prabuddha Chakraborty. "SALTY: Explainable Artificial Intelligence Guided Structural Analysis for Hardware Trojan Detection." In 2025 IEEE 43rd VLSI Test Symposium (VTS). IEEE, 2025. https://doi.org/10.1109/vts65138.2025.11022818.

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Li, Zhanguo, Wu Bao, Bo Yu, and Yan Wang. "GPU Energy Efficiency Management Based on Embedded Software and Hardware Systems." In 2024 8th Asian Conference on Artificial Intelligence Technology (ACAIT). IEEE, 2024. https://doi.org/10.1109/acait63902.2024.11022195.

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Roberge, P. R. "Searching for Corrosion Intelligence." In CORROSION 1999. NACE International, 1999. https://doi.org/10.5006/c1999-99232.

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Abstract The incredible progress in computing power and availability has created a tremendous wealth of information available at the touch of a few buttons. However, such wealth can easily provoke what is commonly described as ‘information overload.’ The massive number of connections produced by a single search of the Web, for example, can greatly overwhelm users of this new technology. The rapidity of Web searches is due to the synergy between progress made in network connectivity protocols, intelligent search strategies and supporting hardware. This paper will attempt to define the basic ele
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Wang, Jiangdong, Lixin Yu, Wei Zhuang, Shiyuan Zhang, Xue Yang, and Daming Yang. "Research on hardware acceleration technology based on SPARC microprocessor." In Fourth International Conference on Electronics Technology and Artificial Intelligence (ETAI 2025), edited by Shaohua Luo and Akash Saxena. SPIE, 2025. https://doi.org/10.1117/12.3068979.

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Yang, Kun, Cheng Chen, Hongshuai Liu, et al. "Hardware design of video transcoding based on RISC-V architecture." In Fourth International Conference on Electronics Technology and Artificial Intelligence (ETAI 2025), edited by Shaohua Luo and Akash Saxena. SPIE, 2025. https://doi.org/10.1117/12.3068408.

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Sun, Dongqing, Mingming Yu, Zhen Guo, and Tianling Liu. "Hardware Security Vulnerability Mining Techniques for Intelligent Connected Vehicles." In 2024 3rd International Conference on Artificial Intelligence, Internet of Things and Cloud Computing Technology (AIoTC). IEEE, 2024. http://dx.doi.org/10.1109/aiotc63215.2024.10748330.

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Reports on the topic "Hardware for Artificial Intelligence"

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Lohn, Andrew, and Micah Musser. AI and Compute: How Much Longer Can Computing Power Drive Artificial Intelligence Progress? Center for Security and Emerging Technology, 2022. http://dx.doi.org/10.51593/2021ca009.

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Between 2012 and 2018, the amount of computing power used by record-breaking artificial intelligence models doubled every 3.4 months. Even with money pouring into the AI field, this trendline is unsustainable. Because of cost, hardware availability and engineering difficulties, the next decade of AI can't rely exclusively on applying more and more computing power to drive further progress.
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Chernavskikh, Vladislav. Nuclear Weapons and Artificial Intelligence: Technological Promises and Practical Realities. Stockholm International Peace Research Institute, 2024. http://dx.doi.org/10.55163/vbqx6088.

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Recent advances in the capabilities of artificial intelligence (AI) have increased state interest in leveraging AI for military purposes. Military integration of advanced AI by nuclear-armed states has the potential to have an impact on elements of their nuclear deterrence architecture such as missile early-warning systems, intelligence, surveillance and reconnaissance (ISR) and nuclear command, control and communications (NC3), as well as related conventional systems. At the same time, a number of technological and logistical factors can potentially limit or slow the adoption of AI in the nuc
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Musser, Micah, Rebecca Gelles, Catherine Aiken, and Andrew Lohn. “The Main Resource is the Human”. Center for Security and Emerging Technology, 2023. http://dx.doi.org/10.51593/20210071.

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Progress in artificial intelligence (AI) depends on talented researchers, well-designed algorithms, quality datasets, and powerful hardware. The relative importance of these factors is often debated, with many recent “notable” models requiring massive expenditures of advanced hardware. But how important is computational power for AI progress in general? This data brief explores the results of a survey of more than 400 AI researchers to evaluate the importance and distribution of computational needs.
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Pasupuleti, Murali Krishna. Quantum-Enhanced Machine Learning: Harnessing Quantum Computing for Next-Generation AI Systems. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv125.

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Abstract Quantum-enhanced machine learning (QML) represents a paradigm shift in artificial intelligence by integrating quantum computing principles to solve complex computational problems more efficiently than classical methods. By leveraging quantum superposition, entanglement, and parallelism, QML has the potential to accelerate deep learning training, optimize combinatorial problems, and enhance feature selection in high-dimensional spaces. This research explores foundational quantum computing concepts relevant to AI, including quantum circuits, variational quantum algorithms, and quantum k
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Ruvinsky, Alicia, Timothy Garton, Daniel Chausse, Rajeev Agrawal, Harland Yu, and Ernest Miller. Accelerating the tactical decision process with High-Performance Computing (HPC) on the edge : motivation, framework, and use cases. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/42169.

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Managing the ever-growing volume and velocity of data across the battlefield is a critical problem for warfighters. Solving this problem will require a fundamental change in how battlefield analyses are performed. A new approach to making decisions on the battlefield will eliminate data transport delays by moving the analytical capabilities closer to data sources. Decision cycles depend on the speed at which data can be captured and converted to actionable information for decision making. Real-time situational awareness is achieved by locating computational assets at the tactical edge. Acceler
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anis, sehab. Artificial Intelligence. ResearchHub Technologies, Inc., 2023. http://dx.doi.org/10.55277/researchhub.agwfnyrw.

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Roberts, Kamie. Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile. National Institute of Standards and Technology, 2024. http://dx.doi.org/10.6028/nist.ai.600-1.

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Novak, Jr, Simmons Gordon S., Porter Robert F., Kumar Bruce W., Causey Vipin, and Robert L. Artificial Intelligence Project. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada230793.

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Guerreiro, Joao, Sergio Rebelo, and Pedro Teles. Regulating Artificial Intelligence. National Bureau of Economic Research, 2023. http://dx.doi.org/10.3386/w31921.

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Cwik, Cynthia, Paul Grimm, Maura Grossman, and Toby Walsh. Artificial Intelligence and the Courts: Artificial Intelligence Trustworthiness, and Litigation. American Association for the Advancement of Science, 2022. http://dx.doi.org/10.1126/aaas.adf0786.

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