Letteratura scientifica selezionata sul tema "Hardware for Artificial Intelligence"
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Articoli di riviste sul tema "Hardware for Artificial Intelligence":
Burkert, Andreas. "Hardware for Artificial Intelligence". ATZ worldwide 121, n. 5 (26 aprile 2019): 8–13. http://dx.doi.org/10.1007/s38311-019-0060-0.
Burkert, Andreas. "Hardware for Artificial Intelligence". ATZelectronics worldwide 14, n. 3 (marzo 2019): 8–13. http://dx.doi.org/10.1007/s38314-019-0026-4.
Popov, I. "SoC hardware supporting artificial intelligence". ELECTRONICS: Science, Technology, Business, n. 7 (2018): 116–23. http://dx.doi.org/10.22184/1992-4178.2018.178.7.116.123.
VerWey, John. "The Other Artificial Intelligence Hardware Problem". Computer 55, n. 1 (gennaio 2022): 34–42. http://dx.doi.org/10.1109/mc.2021.3113271.
Prati, Enrico. "Quantum neuromorphic hardware for quantum artificial intelligence". Journal of Physics: Conference Series 880 (agosto 2017): 012018. http://dx.doi.org/10.1088/1742-6596/880/1/012018.
Yoon, Young Hyun, Dong Hyun Hwang, Jun Hyeok Yang e Seung Eun Lee. "Intellino: Processor for Embedded Artificial Intelligence". Electronics 9, n. 7 (18 luglio 2020): 1169. http://dx.doi.org/10.3390/electronics9071169.
Wang, Xiaoyin. "Artificial intelligence enhanced environmental detection system". Applied and Computational Engineering 66, n. 1 (29 maggio 2024): 156–59. http://dx.doi.org/10.54254/2755-2721/66/20240938.
HNATCHUK, YELYZAVETA, YEVHENIY SIERHIEIEV e ALINA HNATCHUK. "USING ARTIFICIAL INTELLIGENCE ACCELERATORS TO TRAIN COMPUTER GAME CHARACTERS". Computer systems and information technologies, n. 1 (21 agosto 2021): 63–70. http://dx.doi.org/10.31891/csit-2021-3-9.
Smith, Adam Leon. "Artificial Intelligence". ITNOW 64, n. 3 (19 agosto 2022): 47. http://dx.doi.org/10.1093/combul/bwac093.
Smith, Adam Leon. "Artificial Intelligence". ITNOW 64, n. 2 (12 maggio 2022): 65. http://dx.doi.org/10.1093/itnow/bwac065.
Tesi sul tema "Hardware for Artificial Intelligence":
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.
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.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 93-96).
Dell's target to provide quality products based on reliability, security, and manageability, has driven Dell Inc. to become one of the largest PC suppliers. The recent developments in Artificial Intelligence (AI) combined with a competitive market situation have encouraged Dell to research new opportunities. Al research and breakthroughs have risen in the last years, bringing along revolutionary technologies and companies that are disrupting all businesses. Over 30 potential concepts for Al integration at Dell Inc. were identified and evaluated to select the ones with the highest potential. The top-most concept consisted of preventing in real time the failure of hardware. This concept was investigated using a data science process. Currently, there exist a number of machine learning tools that automate the last stages of the proposed data science process to create predictive models. The utilized tools vary in functionality and evaluation standards, but also provide other services such as data and model storage and visualization options. The proposed solution utilizes the deep feature synthesis algorithm that automatically generates features from problem-specific data. These engineered features boosted predictive model accuracy by an average of 10% for the AUC and up to 250% in recall for test (out of sample) data. The proposed solution estimates an impact exceeding $407M in the first five years for Dell Inc. and all of the involved suppliers. Conservatively, the direct impact on Dell Inc. is particular to batteries under warranty and is expected to surpass $2.7M during the first five years. The conclusions show a high potential for implementation.
by Mario Orozco Gabriel.
M.B.A.
S.M. in Engineering Systems
Cheng, Chih Kang. "Hardware implementation of the complex Hopfield neural network". CSUSB ScholarWorks, 1995. https://scholarworks.lib.csusb.edu/etd-project/1016.
GRIMALDI, MATTEO. "Hardware-Aware Compression Techniques for Embedded Deep Neural Networks". Doctoral thesis, Politecnico di Torino, 2021. http://hdl.handle.net/11583/2933756.
Bedi, Abhishek. "A generic platform for the evolution of hardware". Click here to access this resource online, 2009. http://hdl.handle.net/10292/651.
MARRONE, FRANCESCO. "Memristor-based hardware accelerators: from device modeling to AI applications". Doctoral thesis, Politecnico di Torino, 2022. http://hdl.handle.net/11583/2972305.
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.
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.
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.
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.
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.
Libri sul tema "Hardware for Artificial Intelligence":
Mishra, Ashutosh, Jaekwang Cha, Hyunbin Park e Shiho Kim, a cura di. Artificial Intelligence and Hardware Accelerators. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-22170-5.
Adamatzky, Andrew. Artificial life models in hardware. Dordrecht: Springer, 2009.
Kropf, Thomas. Introduction to Formal Hardware Verification. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999.
Baofu, Peter. The future of post-human computing: A preface to a new theory of hardware, software and the mind. Great Abington, Cambridge, UK: Cambridge International Science Publishing, 2011.
Jovanović, Aleksandar S. Expert Systems in Structural Safety Assessment: Proceedings of an International Course October 2-4, 1989, Stuttgart, FRG. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989.
Lee, Bang W. Hardware annealing in analog VLSI neurocomputing. Boston: Kluwer Academic Publishers, 1991.
Eder, Kerstin. Hardware and Software: Verification and Testing: 7th International Haifa Verification Conference, HVC 2011, Haifa, Israel, December 6-8, 2011, Revised Selected Papers. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.
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. Cham: Springer Nature, 2019.
Sood, A. K. Active Perception and Robot Vision. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992.
Oman) ICC (Conference : Oman) (1st 2014 Muscat. Intelligent cloud computing: First International Conference, ICC 2014, Muscat, Oman, February 24-26, 2014, Revised selected papers. Cham: Springer, 2015.
Capitoli di libri sul tema "Hardware for Artificial Intelligence":
Mishra, Ashutosh, Pamul Yadav e Shiho Kim. "Artificial Intelligence Accelerators". In Artificial Intelligence and Hardware Accelerators, 1–52. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-22170-5_1.
Yadav, Pamul, Ashutosh Mishra e Shiho Kim. "Neuromorphic Hardware Accelerators". In Artificial Intelligence and Hardware Accelerators, 225–68. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-22170-5_8.
Lippmann, Bernhard, Matthias Ludwig e Horst Gieser. "Generating Trust in Hardware through Physical Inspection". In Embedded Artificial Intelligence, 45–59. New York: River Publishers, 2023. http://dx.doi.org/10.1201/9781003394440-5.
Liu, Yanli, Bochen Guan, Weiyi Li, Qinwen Xu e Shuxue Quan. "SMOF: Squeezing More Out of Filters Yields Hardware-Friendly CNN Pruning". In Artificial Intelligence, 242–54. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20497-5_20.
Burns, Jeff. "The New Era of AI Hardware". In From Artificial Intelligence to Brain Intelligence, 55–65. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003338215-4.
Jhung, Junekyo, Ho Suk, Hyungbin Park e Shiho Kim. "Hardware Accelerators for Autonomous Vehicles". In Artificial Intelligence and Hardware Accelerators, 269–317. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-22170-5_9.
Kim, Jinhyuk, e Shiho Kim. "Hardware Accelerators in Embedded Systems". In Artificial Intelligence and Hardware Accelerators, 167–81. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-22170-5_6.
Nedjah, Nadia, e Luiza de Macedo Mourelle. "Hardware Architecture for Genetic Algorithms". In Innovations in Applied Artificial Intelligence, 554–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11504894_76.
Dimopoulos, Alexandros, Christos Pavlatos, Ioannis Panagopoulos e George Papakonstantinou. "An Efficient Hardware Implementation for AI Applications". In Advances in Artificial Intelligence, 35–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11752912_6.
Abidi, Taha Yassine, Iyad Dayoub, Elhadj Doguech e Ihsen Alouani. "Federated Learning: Privacy, Security and Hardware Perspectives". In Advancing Edge Artificial Intelligence, 65–86. New York: River Publishers, 2024. http://dx.doi.org/10.1201/9781003478713-3.
Atti di convegni sul tema "Hardware for Artificial Intelligence":
Dally, William J., C. Thomas Gray, John Poulton, Brucek Khailany, John Wilson e Larry Dennison. "Hardware-Enabled Artificial Intelligence". In 2018 IEEE Symposium on VLSI Circuits. IEEE, 2018. http://dx.doi.org/10.1109/vlsic.2018.8502368.
"Hardware for AI". In Emerging Topics in Artificial Intelligence (ETAI) 2021, a cura di Giovanni Volpe, Joana B. Pereira, Daniel Brunner e Aydogan Ozcan. SPIE, 2021. http://dx.doi.org/10.1117/12.2606000.
Dinu, A., e P. L. Ogrutan. "Opportunities of using artificial intelligence in hardware verification". In 2019 IEEE 25th International Symposium for Design and Technology in Electronic Packaging (SIITME). IEEE, 2019. http://dx.doi.org/10.1109/siitme47687.2019.8990751.
Fojtik, Rostislav. "USING HARDWARE TO SUPPORT ARTIFICIAL INTELLIGENCE IN EDUCATION". In 18th International Technology, Education and Development Conference. IATED, 2024. http://dx.doi.org/10.21125/inted.2024.1170.
Lim, Kah Yee, Joan Hau e Yiqi Tew. "Computer Performance Evaluation for Virtual Classroom with Artificial Intelligence Features". In International Conference on Digital Transformation and Applications (ICDXA 2021). Tunku Abdul Rahman University College, 2021. http://dx.doi.org/10.56453/icdxa.2021.1008.
Adamov, Andrey Anatolievich, e Leonid Konstantinovich Eisymont. "Variants of hardware architectural solutions for artificial intelligence systems". In 3rd International Conference “Futurity designing. Digital reality problems”. Keldysh Institute of Applied Mathematics, 2020. http://dx.doi.org/10.20948/future-2020-10.
Romero, A., C. Heras, M. Vega, J. Naranjo, C. Vázquez e A. Preciado. "Intelligent Open-Hardware ECG Platform for the Heart Patients Control and Diagnosis". In Artificial Intelligence and Applications. Calgary,AB,Canada: ACTAPRESS, 2010. http://dx.doi.org/10.2316/p.2010.674-144.
Attri, Anant, Sandhya Verma, Shweta Pandey, Yerrolla Chanti, Mansi Sahu e Rajat Balyan. "Exhilarating Growth in Education Through Artificial Intelligence". In 2023 International Conference on Quantum Technologies, Communications, Computing, Hardware and Embedded Systems Security (iQ-CCHESS). IEEE, 2023. http://dx.doi.org/10.1109/iq-cchess56596.2023.10391474.
Singh, Vinayak, Abhiranjan Dixit, Shweta Pandey, Bura Vijay Kumar, Vikrant Pachouri e Mansi Sahu. "Role of Artificial Intelligence in Criminal Investigation". In 2023 International Conference on Quantum Technologies, Communications, Computing, Hardware and Embedded Systems Security (iQ-CCHESS). IEEE, 2023. http://dx.doi.org/10.1109/iq-cchess56596.2023.10391288.
Burguete-Lopez, Arturo, Maksim Makarenko, Qizhou Wang, Fedor Getman e Andrea Fratalocchi. "Artificial-Intelligence Empowered Universal Metrology Optical Camera". In CLEO: Applications and Technology. Washington, D.C.: Optica Publishing Group, 2023. http://dx.doi.org/10.1364/cleo_at.2023.jtu2a.25.
Rapporti di organizzazioni sul tema "Hardware for Artificial Intelligence":
Lohn, Andrew, e Micah Musser. AI and Compute: How Much Longer Can Computing Power Drive Artificial Intelligence Progress? Center for Security and Emerging Technology, gennaio 2022. http://dx.doi.org/10.51593/2021ca009.
Musser, Micah, Rebecca Gelles, Catherine Aiken e Andrew Lohn. “The Main Resource is the Human”. Center for Security and Emerging Technology, aprile 2023. http://dx.doi.org/10.51593/20210071.
Ruvinsky, Alicia, Timothy Garton, Daniel Chausse, Rajeev Agrawal, Harland Yu e 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.), settembre 2021. http://dx.doi.org/10.21079/11681/42169.
anis, sehab. Artificial Intelligence. ResearchHub Technologies, Inc., agosto 2023. http://dx.doi.org/10.55277/researchhub.agwfnyrw.
Roberts, Kamie. Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile. Gaithersburg, MD: National Institute of Standards and Technology, 2024. http://dx.doi.org/10.6028/nist.ai.600-1.
Novak, Jr, Simmons Gordon S., Porter Robert F., Kumar Bruce W., Causey Vipin e Robert L. Artificial Intelligence Project. Fort Belvoir, VA: Defense Technical Information Center, gennaio 1990. http://dx.doi.org/10.21236/ada230793.
Guerreiro, Joao, Sergio Rebelo e Pedro Teles. Regulating Artificial Intelligence. Cambridge, MA: National Bureau of Economic Research, novembre 2023. http://dx.doi.org/10.3386/w31921.
Cwik, Cynthia, Paul Grimm, Maura Grossman e Toby Walsh. Artificial Intelligence and the Courts: Artificial Intelligence Trustworthiness, and Litigation. American Association for the Advancement of Science, settembre 2022. http://dx.doi.org/10.1126/aaas.adf0786.
Karanicolas, Michael, e Mallory Knodel. Artificial Intelligence and the Courts: Artificial Intelligence and Bias - An Evaluation. American Association for the Advancement of Science, settembre 2022. http://dx.doi.org/10.1126/aaas.adf0788.
Firth-Butterfield, Kay, e Karen Silverman. Artificial Intelligence and the Courts: Artificial Intelligence - Foundational Issues and Glossary. American Association for the Advancement of Science, settembre 2022. http://dx.doi.org/10.1126/aaas.adf0782.