Zobrazeno 1 - 10
of 135
pro vyhledávání: '"Maria Puglisi"'
Autor:
Daniela Gandolfi, Francesco Maria Puglisi, Alexander Serb, Michele Giugliano, Jonathan Mapelli
Publikováno v:
Frontiers in Cellular Neuroscience, Vol 16 (2022)
Externí odkaz:
https://doaj.org/article/aadb6aa692b249f08c79bbb7adaa81bf
Publikováno v:
IEEE Journal of the Electron Devices Society, Vol 8, Pp 757-764 (2020)
Low-power smart devices are becoming pervasive in our world. Thus, relevant research efforts are directed to the development of innovative low power computing solutions that enable in-memory computations of logic-operations, thus avoiding the von Neu
Externí odkaz:
https://doaj.org/article/2442bf913a854e8388d68b8045ab3673
Publikováno v:
Proceedings of the IEEE. 111:158-184
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::751f329401b9a7d5a51666d3e6e1fc19
https://doi.org/10.1109/jproc.2023.3234607
https://doi.org/10.1109/jproc.2023.3234607
Autor:
Sebastian Pazos, Wenwen Zheng, Tommaso Zanotti, Fernando Aguirre, Thales Becker, Yaqing Shen, Kaichen Zhu, Yue Yuan, Gilson Wirth, Francesco Maria Puglisi, Juan Bautista Roldán, Felix Palumbo, Mario Lanza
Publikováno v:
Nanoscale. 15:2171-2180
The development of the internet-of-things requires cheap, light, small and reliable true random number generator (TRNG) circuits to encrypt the data—generated by objects or humans—before transmitting them. However, all current solutions consume t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1029b10dc6bb1e705a1689996f95cc9d
https://doi.org/10.1039/d2nr06222d
https://doi.org/10.1039/d2nr06222d
Publikováno v:
IEEE Transactions on Electron Devices. 69:6991-6998
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5a6eee62307dc4458e6f1dc6fe350879
https://doi.org/10.1109/ted.2022.3213502
https://doi.org/10.1109/ted.2022.3213502
Publikováno v:
Micromachines, Vol 12, Iss 10, p 1243 (2021)
Logic-in-memory (LIM) circuits based on the material implication logic (IMPLY) and resistive random access memory (RRAM) technologies are a candidate solution for the development of ultra-low power non-von Neumann computing architectures. Such archit
Externí odkaz:
https://doaj.org/article/6a2dd6d171014da3849b6cc9158cbb06
Publikováno v:
Journal of Low Power Electronics and Applications, Vol 11, Iss 3, p 29 (2021)
Different in-memory computing paradigms enabled by emerging non-volatile memory technologies are promising solutions for the development of ultra-low-power hardware for edge computing. Among these, SIMPLY, a smart logic-in-memory architecture, provid
Externí odkaz:
https://doaj.org/article/c456e3c2adf64c689659cea8f0ba1b87
Akademický článek
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Publikováno v:
Micromachines, Vol 12, Iss 6, p 709 (2021)
The intentional doping of lateral GaN power high electron mobility transistors (HEMTs) with carbon (C) impurities is a common technique to reduce buffer conductivity and increase breakdown voltage. Due to the introduction of trap levels in the GaN ba
Externí odkaz:
https://doaj.org/article/fdf2e3b467b24b1d9767ca0fe9bea2ff
In-memory computing hardware accelerators for binarized neural networks based on resistive RAM (RRAM) memory technologies represent a promising solution for enabling the execution of deep neural network algorithms on resource-constrained devices at t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ab2024b59c9b8ab4f08794b0913b2d7e
https://mts.intechopen.com/articles/show/title/study-of-rram-based-binarized-neural-networks-inference-accelerators-using-an-rram-physics-based-com
https://mts.intechopen.com/articles/show/title/study-of-rram-based-binarized-neural-networks-inference-accelerators-using-an-rram-physics-based-com
