Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Mohamed Ayoub Neggaz"'
Autor:
Junjie Li, Alexander Bobyr, Swen Boehm, William Brantley, Holger Brunst, Aurelien Cavelan, Sunita Chandrasekaran, Jimmy Cheng, Florina M. Ciorba, Mathew Colgrove, Tony Curtis, Christopher Daley, Mauricio Ferrato, Mayara Gimenes de Souza, Nick Hagerty, Robert Henschel, Guido Juckeland, Jeffrey Kelling, Kelvin Li, Ron Lieberman, Kevin McMahon, Egor Melnichenko, Mohamed Ayoub Neggaz, Hiroshi Ono, Carl Ponder, Dave Raddatz, Severin Schueller, Robert Searles, Fedor Vasilev, Veronica Melesse Vergara, Bo Wang, Bert Wesarg, Sandra Wienke, Miguel Zavala
Publikováno v:
Companion of the 2022 ACM/SPEC International Conference on Performance Engineering.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::961187831b1d199d15968cc63f27e783
https://doi.org/10.1145/3491204.3527498
https://doi.org/10.1145/3491204.3527498
Publikováno v:
IEEE Design & Test
IEEE Design & Test, 2020, 37 (2), pp.76-83. ⟨10.1109/MDAT.2019.2952336⟩
IEEE Design & Test, IEEE, 2020, 37 (2), pp.76-83. ⟨10.1109/MDAT.2019.2952336⟩
IEEE Design & Test, 2020, 37 (2), pp.76-83. ⟨10.1109/MDAT.2019.2952336⟩
IEEE Design & Test, IEEE, 2020, 37 (2), pp.76-83. ⟨10.1109/MDAT.2019.2952336⟩
International audience; Resource-constrained CNN implementations are subject to various reliability threats. This article provides an exploratory study investigating the impact of faults (soft errors modeled as bit flips) across the parameters of CNN
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a9cadc5c9371d31c56c6e3099f9339a5
https://hal.science/hal-03321520
https://hal.science/hal-03321520
Publikováno v:
Proceedings of the INTelligent Embedded Systems Architectures and Applications Workshop 2019.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5b3084fdec953b0e417b976f7e573471
https://doi.org/10.1145/3372394.3372400
https://doi.org/10.1145/3372394.3372400
Publikováno v:
2018 30th International Conference on Microelectronics (ICM)
2018 30th International Conference on Microelectronics (ICM), Dec 2018, Sousse, Tunisia. pp.68-71, ⟨10.1109/ICM.2018.8704033⟩
ICM
2018 30th International Conference on Microelectronics (ICM), Dec 2018, Sousse, Tunisia. pp.68-71, ⟨10.1109/ICM.2018.8704033⟩
ICM
International audience; State-of-the-art Convolutional Neural Networks (CNN) are used to process images. In most cases, videos are streamed and processed frame by frame using a CNN. In this paper we present a two-step approach to process images in a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9285e9d8e9cb7ff4122b4b7a95d6af41
https://hal-uphf.archives-ouvertes.fr/hal-03382375
https://hal-uphf.archives-ouvertes.fr/hal-03382375
Publikováno v:
IEEE-ACM Design, Automation & Test in Europe Conference & Exhibition (DATE'18)
IEEE-ACM Design, Automation & Test in Europe Conference & Exhibition (DATE'18), Mar 2018, Dresden, Germany. pp.985-990, ⟨10.23919/DATE.2018.8342152⟩
DATE
IEEE-ACM Design, Automation & Test in Europe Conference & Exhibition (DATE'18), Mar 2018, Dresden, Germany. pp.985-990, ⟨10.23919/DATE.2018.8342152⟩
DATE
International audience; Memory hierarchy latency is one of the main problems that prevents processors from achieving high performance. To eliminate the need of loading/storing large sets of data, Resistive Associative Processors (ReAP) have been prop
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bd0371ec5f5daa1ad4862d1ddbad67fc
https://hal-uphf.archives-ouvertes.fr/hal-03383708
https://hal-uphf.archives-ouvertes.fr/hal-03383708
Publikováno v:
2018 IEEE 36th International Conference on Computer Design (ICCD)
2018 IEEE 36th International Conference on Computer Design (ICCD), Oct 2018, Orlando, United States. pp.476-479, ⟨10.1109/ICCD.2018.00077⟩
ICCD
2018 IEEE 36th International Conference on Computer Design (ICCD), Oct 2018, Orlando, United States. pp.476-479, ⟨10.1109/ICCD.2018.00077⟩
ICCD
International audience; Deep learning systems such as Convolutional Neural Networks (CNNs) have shown remarkable efficiency in dealing with a variety of complex real life problems. To accelerate the execution of these heavy algorithms, a plethora of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::da72acda2e35eee71c5408fe262e37c2