Zobrazeno 1 - 10
of 55
pro vyhledávání: '"G. Piccolboni"'
Autor:
S. Bianchi, I. Muñoz-Martin, E. Covi, A. Bricalli, G. Piccolboni, A. Regev, G. Molas, J. F. Nodin, F. Andrieu, D. Ielmini
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-14 (2023)
A big challenge for artificial intelligence is to gain the ability of learning by experience like biological systems. Here Bianchi et al. propose a hardware neural network based on resistive-switching synaptic arrays which dynamically adapt to the en
Externí odkaz:
https://doaj.org/article/1fd89242574943b3ba52bdec76007f2c
Autor:
L. Reganaz, D. Deleruyelle, Q. Rafhay, J. Minguet Lopez, N. Castellani, J. F. Nodin, A. Bricalli, G. Piccolboni, G. Molas, F. Andrieu
Publikováno v:
2023 IEEE International Reliability Physics Symposium (IRPS).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::321b56f0a9151384af9e1c720e5b3e21
https://doi.org/10.1109/irps48203.2023.10117882
https://doi.org/10.1109/irps48203.2023.10117882
Autor:
Amir Regev, D. Green, G. Piccolboni, Alessandro Bricalli, W. Goes, P. Blaise, G. Molas, J. F. Nodin
Publikováno v:
2021 IEEE International Memory Workshop (IMW).
During the last few years, oxide-based ReRAM technology has attracted intense industrial and scientific research interest. Therefore, we have performed an in-depth computational study with a focus on data retention besides the resistive switching and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::20becfe5b90f3fb07da1aae129863d3f
https://doi.org/10.1109/imw51353.2021.9439623
https://doi.org/10.1109/imw51353.2021.9439623
Autor:
Alexandre Valentian, Amir Regev, Thomas Mesquida, Alessandro Bricalli, G. Piccolboni, Jean-Francois Nodin, Gabriel Molas
Publikováno v:
AICAS
This paper presents, to the best of the authors’ knowledge, the first complete integration of a Spiking Neural Network combining analog neurons and SiO x -based resistive memory (RRAM) synapses. The implemented topology is a perceptron, and the cir
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::133955bd0907cf7acf743f78934ed324
https://doi.org/10.1109/aicas48895.2020.9073840
https://doi.org/10.1109/aicas48895.2020.9073840
Autor:
Alessandro Bricalli, Daniele Ielmini, Erika Covi, E. Nowak, Amir Regev, G. Piccolboni, G. Molas, S. Bianchi, Irene Munoz-Martin, Jean-Francois Nodin
Publikováno v:
2020 IEEE Symposium on VLSI Technology.
Biological systems autonomously evolve to maximize their efficiency in a continually changing world. On the other hand, artificial neural networks (ANNs) outperform the human ability of object recognition but cannot acquire new information without fo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cf301cdd4bbd5aa2a61bdc63353ad72d
https://doi.org/10.1109/vlsitechnology18217.2020.9265072
https://doi.org/10.1109/vlsitechnology18217.2020.9265072
Akademický článek
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Akademický článek
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Akademický článek
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Autor:
C. Cagli, Elisa Vianello, G. Reimbold, G. Piccolboni, Daniele Garbin, Alessandro Grossi, Cristian Zambelli, G. Molas
Publikováno v:
2017 Joint International EUROSOI Workshop and International Conference on Ultimate Integration on Silicon (EUROSOI-ULIS).
Resistive memories (RRAM) are attracting a wide interest as candidates for the next generation memory technology, in particular for embedded and, more generally, for low power applications like IoT. However, their variability still remains a concern
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::df5859c14449f545726fefaa2e958988
https://doi.org/10.1109/ulis.2017.7962593
https://doi.org/10.1109/ulis.2017.7962593
Autor:
Blaise Yvert, Jean-Michel Portal, Thilo Werner, Luca Perniola, G. Piccolboni, Daniele Garbin, Olivier Bichler, Gabriel Molas, Barbara De Salvo, Elisa Vianello
Publikováno v:
Neuro-inspired Computing Using Resistive Synaptic Devices ISBN: 9783319543123
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::41647a8fc793f27ff39f2a36d5befa80
https://doi.org/10.1007/978-3-319-54313-0_14
https://doi.org/10.1007/978-3-319-54313-0_14
