Showing 41 - 60 results of 133 for search '"Computer architecture"', query time: 0.11s Refine Results
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    Cooling Trapped Ions with Phonon Rapid Adiabatic Passage by M. I. Fabrikant, P. Lauria, I. S. Madjarov, W. C. Burton, R. T. Sutherland

    Published 2024-11-01
    “…In recent demonstrations of the quantum charge-coupled device computer architecture, circuit times are dominated by cooling. …”
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    An Educational RISC-V-Based 16-Bit Processor by Jecel Mattos de Assumpção, Oswaldo Hideo Ando, Hugo Puertas de Araújo, Mario Gazziro

    Published 2024-11-01
    “…By working on this project, students can gain hands-on experience with digital logic design, Verilog programming, and computer architecture. The project also includes tools and scripts to help students transform assembly code into binary format, making it easier for them to test and verify their designs. …”
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    MIMD Programs Execution Support on SIMD Machines: A Holistic Survey by Dheya Mustafa, Ruba Alkhasawneh, Fadi Obeidat, Ahmed S. Shatnawi

    Published 2024-01-01
    “…This study will be beneficial for developers and researchers in the field of computer architecture and parallel computing of intensive scientific applications, specifically for early-stage high-performance computing researchers, to obtain a brief overview of performance optimization opportunities as well as the challenges of existing SIMD platforms.…”
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    On a Simplified Approach to Achieve Parallel Performance and Portability Across CPU and GPU Architectures by Nathaniel Morgan, Caleb Yenusah, Adrian Diaz, Daniel Dunning, Jacob Moore, Erin Heilman, Calvin Roth, Evan Lieberman, Steven Walton, Sarah Brown, Daniel Holladay, Marko Knezevic, Gavin Whetstone, Zachary Baker, Robert Robey

    Published 2024-10-01
    “…This paper presents software advances to easily exploit computer architectures consisting of a multi-core CPU and CPU+GPU to accelerate diverse types of high-performance computing (HPC) applications using a single code implementation. …”
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    Astronomy potential of KM3NeT/ARCA by KM3NeT Collaboration, S. Aiello, A. Albert, M. Alshamsi, S. Alves Garre, Z. Aly, A. Ambrosone, F. Ameli, M. Andre, E. Androutsou, M. Anguita, L. Aphecetche, M. Ardid, S. Ardid, H. Atmani, J. Aublin, F. Badaracco, L. Bailly-Salins, Z. Bardačová, B. Baret, A. Bariego-Quintana, A. Baruzzi, S. Basegmez du Pree, Y. Becherini, M. Bendahman, F. Benfenati, M. Benhassi, D. M. Benoit, E. Berbee, V. Bertin, S. Biagi, M. Boettcher, D. Bonanno, J. Boumaaza, M. Bouta, M. Bouwhuis, C. Bozza, R. M. Bozza, H. Brânzaş, F. Bretaudeau, M. Breuhaus, R. Bruijn, J. Brunner, R. Bruno, E. Buis, R. Buompane, J. Busto, B. Caiffi, D. Calvo, S. Campion, A. Capone, F. Carenini, V. Carretero, T. Cartraud, P. Castaldi, V. Cecchini, S. Celli, L. Cerisy, M. Chabab, M. Chadolias, A. Chen, S. Cherubini, T. Chiarusi, M. Circella, R. Cocimano, J. A. B. Coelho, A. Coleiro, R. Coniglione, P. Coyle, A. Creusot, G. Cuttone, R. Dallier, Y. Darras, A. De Benedittis, B. De Martino, V. Decoene, R. Del Burgo, I. Del Rosso, L. S. Di Mauro, I. Di Palma, A. F. Díaz, C. Diaz, D. Diego-Tortosa, C. Distefano, A. Domi, C. Donzaud, D. Dornic, M. Dörr, E. Drakopoulou, D. Drouhin, J.-G. Ducoin, R. Dvornický, T. Eberl, E. Eckerová, A. Eddymaoui, T. van Eeden, M. Eff, D. van Eijk, I. El Bojaddaini, S. El Hedri, A. Enzenhöfer, G. Ferrara, M. D. Filipović, F. Filippini, D. Franciotti, L. A. Fusco, J. Gabriel, S. Gagliardini, T. Gal, J. García Méndez, A. Garcia Soto, C. Gatius Oliver, N. Geißelbrecht, H. Ghaddari, L. Gialanella, B. K. Gibson, E. Giorgio, I. Goos, P. Goswami, D. Goupilliere, S. R. Gozzini, R. Gracia, K. Graf, C. Guidi, B. Guillon, M. Gutiérrez, H. van Haren, A. Heijboer, A. Hekalo, L. Hennig, J. J. Hernández-Rey, W. Idrissi Ibnsalih, G. Illuminati, M. de Jong, P. de Jong, B. J. Jung, P. Kalaczyński, O. Kalekin, U. F. Katz, G. Kistauri, C. Kopper, A. Kouchner, V. Kueviakoe, V. Kulikovskiy, R. Kvatadze, M. Labalme, R. Lahmann, G. Larosa, C. Lastoria, A. Lazo, S. Le Stum, G. Lehaut, E. Leonora, N. Lessing, G. Levi, M. Lindsey Clark, F. Longhitano, F. Magnani, J. Majumdar, L. Malerba, F. Mamedov, J. Mańczak, A. Manfreda, M. Marconi, A. Margiotta, A. Marinelli, C. Markou, L. Martin, J. A. Martínez-Mora, F. Marzaioli, M. Mastrodicasa, S. Mastroianni, S. Miccichè, G. Miele, P. Migliozzi, E. Migneco, M. L. Mitsou, C. M. Mollo, L. Morales-Gallegos, M. Morga, A. Moussa, I. Mozun Mateo, R. Muller, M. R. Musone, M. Musumeci, S. Navas, A. Nayerhoda, C. A. Nicolau, B. Nkosi, B. Ó Fearraigh, V. Oliviero, A. Orlando, E. Oukacha, D. Paesani, J. Palacios González, G. Papalashvili, V. Parisi, E. J. Pastor Gomez, A. M. Păun, G. E. Păvălaş, I. Pelegris, S. Peña Martínez, M. Perrin-Terrin, J. Perronnel, V. Pestel, R. Pestes, P. Piattelli, C. Poirè, V. Popa, T. Pradier, J. Prado, S. Pulvirenti, C. A. Quiroz-Rangel, U. Rahaman, N. Randazzo, R. Randriatoamanana, S. Razzaque, I. C. Rea, D. Real, G. Riccobene, J. Robinson, A. Romanov, A. Šaina, F. Salesa Greus, D. F. E. Samtleben, A. Sánchez Losa, S. Sanfilippo, M. Sanguineti, C. Santonastaso, D. Santonocito, P. Sapienza, J. Schnabel, J. Schumann, H. M. Schutte, J. Seneca, N. Sennan, B. Setter, I. Sgura, R. Shanidze, A. Sharma, Y. Shitov, F. Šimkovic, A. Simonelli, A. Sinopoulou, M. V. Smirnov, B. Spisso, M. Spurio, D. Stavropoulos, I. Štekl, M. Taiuti, Y. Tayalati, H. Thiersen, I. Tosta e Melo, E. Tragia, B. Trocmé, V. Tsourapis, A. Tudorache, E. Tzamariudaki, A. Vacheret, A. Valer Melchor, V. Valsecchi, V. Van Elewyck, G. Vannoye, G. Vasileiadis, F. Vazquez de Sola, C. Verilhac, A. Veutro, S. Viola, D. Vivolo, J. Wilms, E. de Wolf, H. Yepes-Ramirez, G. Zarpapis, S. Zavatarelli, A. Zegarelli, D. Zito, J. D. Zornoza, J. Zúñiga, N. Zywucka

    Published 2024-09-01
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    Enabling Parallel Performance and Portability of Solid Mechanics Simulations Across CPU and GPU Architectures by Nathaniel Morgan, Caleb Yenusah, Adrian Diaz, Daniel Dunning, Jacob Moore, Erin Heilman, Evan Lieberman, Steven Walton, Sarah Brown, Daniel Holladay, Russell Marki, Robert Robey, Marko Knezevic

    Published 2024-11-01
    “…As constitutive models grow more complex and simulations scale up in size, harnessing the capabilities of modern computer architectures has become essential for achieving timely results. …”
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    Research on ciphertext search and sharing technology in edge computing mode by Jifeng WANG, Guofeng WANG

    Published 2022-04-01
    “…Aiming at the problem of edge computing data security, a ciphertext search and sharing solution was proposed, where the above-mentioned edge computing advantages were used to achieve user privacy data protection, edge nodes were used to construct encrypted inverted indexes, indexes and keys between edge nodes and cloud computing platforms were securely shared, and ciphertext search, secure data sharing, and dynamic index update were realized without changing the edge computing architecture and cloud computing architecture.Finally, compared to existing schemes, performance and security were analyzed and discussed, which proves that the proposed scheme has high security strength under ciphertext search attack model, and the ciphertext search efficiency and document dynamic update function are taken into account based on encrypted inverted index.…”
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  16. 56

    Probabilistic photonic computing with chaotic light by Frank Brückerhoff-Plückelmann, Hendrik Borras, Bernhard Klein, Akhil Varri, Marlon Becker, Jelle Dijkstra, Martin Brückerhoff, C. David Wright, Martin Salinga, Harish Bhaskaran, Benjamin Risse, Holger Fröning, Wolfram Pernice

    Published 2024-12-01
    “…Our prototype demonstrates the seamless cointegration of a physical entropy source and a computational architecture that enables ultrafast probabilistic computation by parallel sampling.…”
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    DHRCA: A Design of Security Architecture Based on Dynamic Heterogeneous Redundant for System on Wafer by Bo Mei, Zhengbin Zhu, Peijie Li, Bo Zhao

    Published 2024-01-01
    “…In this paper, we propose a computing architecture based on endogenous security theory—dynamic heterogeneous redundant computing architecture (DHRCA) that can tolerate and detect HTs at runtime. …”
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    An In-Memory-Computing Binary Neural Network Architecture With In-Memory Batch Normalization by Prathamesh Prashant Rege, Ming Yin, Sanjay Parihar, Joseph Versaggi, Shashank Nemawarkar

    Published 2024-01-01
    “…This paper describes an in-memory computing architecture that combines full-precision computation for the first and last layers of a neural network while employing binary weights and input activations for the intermediate layers. …”
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    Personalized lightweight distributed network intrusion detection system in fog computing by Tianpeng YE, Xiang LIN, Jianhua LI, Xuankai ZHANG, Liwen XU

    Published 2023-06-01
    “…With the continuous development of Internet of Things (IoT) technology, there is a constant emergency of new IoT applications with low latency, high dynamics, and large bandwidth requirements.This has led to the widespread aggregation of massive devices and information at the network edge, promoting the emergence and deep development of fog computing architecture.However, with the widespread and in-depth application of fog computing architecture, the distributed network security architecture deployed to ensure its security is facing critical challenges brought by fog computing itself, such as the limitations of fog computing node computing and network communication resources, and the high dynamics of fog computing applications, which limit the edge deployment of complex network intrusion detection algorithms.To effectively solve the above problems, a personalized lightweight distributed network intrusion detection system (PLD-NIDS) was proposed based on the fog computing architecture.A large-scale complex network flow intrusion detection model was trained based on the convolutional neural network architecture, and furthermore the network traffic type distribution of each fog computing node was collected.The personalized model distillation algorithm and the weighted first-order Taylor approximation pruning algorithm were proposed to quickly compress the complex model, breaking through the limitation of traditional model compression algorithms that can only provide single compressed models for edge node deployment due to the high compression calculation overhead when facing a large number of personalized nodes.According to experimental results, the proposed PLD-NIDS architecture can achieve fast personalized compression of edge intrusion detection models.Compared with traditional model pruning algorithms, the proposed architecture achieves a good balance between computational loss and model accuracy.In terms of model accuracy, the proposed weighted first-order Taylor approximation pruning algorithm can achieve about 4% model compression ratio improvement under the same 0.2% model accuracy loss condition compared with the traditional first-order Taylor approximation pruning algorithm.…”
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    Using the Functional Reach Test for Probing the Static Stability of Bipedal Standing in Humanoid Robots Based on the Passive Motion Paradigm by Jacopo Zenzeri, Dalia De Santis, Vishwanathan Mohan, Maura Casadio, Pietro Morasso

    Published 2013-01-01
    “…The goal of this paper is to analyze the static stability of a computational architecture, based on the Passive Motion Paradigm, for coordinating the redundant degrees of freedom of a humanoid robot during whole-body reaching movements in bipedal standing. …”
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