Zobrazeno 1 - 10
of 32
pro vyhledávání: '"Alessandro Bricalli"'
Autor:
Stefano Bianchi, Irene Munoz-Martin, Erika Covi, Alessandro Bricalli, Giuseppe Piccoloni, Amir Regev, Gabriel Molas, Jean Francois Nodin, Francois Andrieu, Daniele Ielmini
Publikováno v:
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits, Vol 7, Iss 2, Pp 132-140 (2021)
Nowadays, artificial neural networks (ANNs) can outperform the human brain’s ability in specific tasks. However, ANNs cannot replicate the efficient and low-power learning, adaptation, and consolidation typical of biological organisms. Here, we pre
Externí odkaz:
https://doaj.org/article/37b5a4a1ad6f48178c41617d2bac5f0f
Surface diffusion-limited lifetime of silver and copper nanofilaments in resistive switching devices
Autor:
Wei Wang, Ming Wang, Elia Ambrosi, Alessandro Bricalli, Mario Laudato, Zhong Sun, Xiaodong Chen, Daniele Ielmini
Publikováno v:
Nature Communications, Vol 10, Iss 1, Pp 1-9 (2019)
Resistive random-access memory is operated based on the formation and disruption of nanoscale conductive filaments, but a mechanistic understanding of this process remains unclear. Here, Wang et al. develop a surface-diffusion model to describe lifet
Externí odkaz:
https://doaj.org/article/bb61f6f7fd6e454bbaaca9088c9120c8
Publikováno v:
Advanced Intelligent Systems, Vol 2, Iss 8, Pp n/a-n/a (2020)
In‐memory computing with cross‐point resistive memory arrays has gained enormous attention to accelerate the matrix‐vector multiplication in the computation of data‐centric applications. By combining a cross‐point array and feedback amplifi
Externí odkaz:
https://doaj.org/article/471c53f6c1a84467a6810e0655cdf984
Autor:
Matteo Farronato, Piergiulio Mannocci, Saverio Ricci, Alessandro Bricalli, Margherita Melegari, Christian Monzio Compagnoni, Daniele Ielmini
Publikováno v:
Proceedings of the Neuromorphic Materials, Devices, Circuits and Systems.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9fcb6df0f3dd390ade3bd9c3d96f1266
https://doi.org/10.29363/nanoge.neumatdecas.2023.042
https://doi.org/10.29363/nanoge.neumatdecas.2023.042
Autor:
Alessandro Bricalli, Daniele Ielmini, Jean Francois Nodin, Giuseppe Piccoloni, Francois Andrieu, Amir Regev, S. Bianchi, Irene Munoz-Martin, Erika Covi, Gabriel Molas
Publikováno v:
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits, Vol 7, Iss 2, Pp 132-140 (2021)
Nowadays, artificial neural networks (ANNs) can outperform the human brain’s ability in specific tasks. However, ANNs cannot replicate the efficient and low-power learning, adaptation, and consolidation typical of biological organisms. Here, we pre
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bb8052fe6cc502781ba3322cf20b20b0
https://doi.org/10.1109/jxcdc.2021.3118061
https://doi.org/10.1109/jxcdc.2021.3118061
Autor:
Sandeep Kaur Kingra, Vivek Parmar, Deepak Verma, Alessandro Bricalli, Giuseppe Piccolboni, Gabriel Molas, Amir Regev, Manan Suri
In this work, we propose a fully-binarized XOR-based IMSS (In-Memory Similarity Search) using RRAM (Resistive Random Access Memory) arrays. XOR (Exclusive OR) operation is realized using 2T-2R bitcells arranged along the column in an array. This enab
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5e2217f5013b7f7d081cf75be67d87d2
http://arxiv.org/abs/2208.02651
http://arxiv.org/abs/2208.02651
Autor:
Matteo Farronato, Margherita Melegari, Saverio Ricci, Shahin Hashemkhani, Alessandro Bricalli, Daniele Ielmini
Publikováno v:
Advanced Electronic Materials. 8:2270037
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0d632214c0f4cd0be73417fd9d6c942f
https://doi.org/10.1002/aelm.202270037
https://doi.org/10.1002/aelm.202270037
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
Publikováno v:
AICAS
Thanks to the high parallelism endowed by physical rules, in-memory computing with crosspoint resistive memory arrays has been applied to accelerate typical dataintensive tasks such as the training and inference of deep learning. Recently, it has bee
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f539e5a520c243ed7df7c000b1a0dbea
https://doi.org/10.1109/aicas48895.2020.9073964
https://doi.org/10.1109/aicas48895.2020.9073964