Zobrazeno 1 - 10
of 75
pro vyhledávání: '"Fabien, Alibart"'
Variability has always been a challenge to mitigate in electronics. This especially holds true for organic semiconductors, where reproducibility and long-term stability concerns hinder industrialization. By relying on a bio-inspired computing paradig
Externí odkaz:
http://arxiv.org/abs/2407.19847
Autor:
Raphaël Dawant, Matthieu Gaudreau, Marc-Antoine Roy, Pierre-Antoine Mouny, Matthieu Valdenaire, Pierre Gliech, Javier Arias Zapata, Malek Zegaoui, Fabien Alibart, Dominique Drouin, Serge Ecoffey
Publikováno v:
Micro and Nano Engineering, Vol 23, Iss , Pp 100251- (2024)
In recent years, resistive memories have emerged as a pivotal advancement in the realm of electronics, offering numerous advantages in terms of energy efficiency, scalability, and non-volatility [1]. Characterized by their unique resistive switching
Externí odkaz:
https://doaj.org/article/623ddebe051d45268b491943aaff42f6
Autor:
Kamila Janzakova, Ismael Balafrej, Ankush Kumar, Nikhil Garg, Corentin Scholaert, Jean Rouat, Dominique Drouin, Yannick Coffinier, Sébastien Pecqueur, Fabien Alibart
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-10 (2023)
Abstract Neural networks are powerful tools for solving complex problems, but finding the right network topology for a given task remains an open question. Biology uses neurogenesis and structural plasticity to solve this problem. Advanced neural net
Externí odkaz:
https://doaj.org/article/b5eab42ae8994ee7b74e7112375ea0e7
Autor:
Giuseppe Ciccone, Matteo Cucchi, Yanfei Gao, Ankush Kumar, Lennart Maximilian Seifert, Anton Weissbach, Hsin Tseng, Hans Kleemann, Fabien Alibart, Karl Leo
Publikováno v:
Discover Materials, Vol 2, Iss 1, Pp 1-12 (2022)
Abstract A new paradigm of electronic devices with bio-inspired features is aiming to mimic the brain’s fundamental mechanisms to achieve recognition of very complex patterns and more efficient computational tasks. Networks of electropolymerized de
Externí odkaz:
https://doaj.org/article/d69fd26a44da4782900cbd72d94e9771
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-11 (2022)
Abstract Electropolymerization is a bottom-up materials engineering process of micro/nano-scale that utilizes electrical signals to deposit conducting dendrites morphologies by a redox reaction in the liquid phase. It resembles synaptogenesis in the
Externí odkaz:
https://doaj.org/article/3c2ba00ee7aa4d2fbede2a5f0ea39d1d
Autor:
Kamila Janzakova, Ankush Kumar, Mahdi Ghazal, Anna Susloparova, Yannick Coffinier, Fabien Alibart, Sébastien Pecqueur
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-11 (2021)
Despite advances in brain-inspired computing, existing electronics use top-down processes that do not compare with neural connections in the brain. Here, the authors report an electrically-tunable electropolymerization process that emulates and contr
Externí odkaz:
https://doaj.org/article/9c5d0fcbb91a4dcc94f206981e689a6c
Autor:
Nikhil Garg, Ismael Balafrej, Terrence C. Stewart, Jean-Michel Portal, Marc Bocquet, Damien Querlioz, Dominique Drouin, Jean Rouat, Yann Beilliard, Fabien Alibart
Publikováno v:
Frontiers in Neuroscience, Vol 16 (2022)
This study proposes voltage-dependent-synaptic plasticity (VDSP), a novel brain-inspired unsupervised local learning rule for the online implementation of Hebb’s plasticity mechanism on neuromorphic hardware. The proposed VDSP learning rule updates
Externí odkaz:
https://doaj.org/article/efbbc052db78470eb28c89db261f7e95
Autor:
Victor Yon, Amirali Amirsoleimani, Fabien Alibart, Roger G. Melko, Dominique Drouin, Yann Beilliard
Publikováno v:
Frontiers in Electronics, Vol 3 (2022)
Novel computing architectures based on resistive switching memories (also known as memristors or RRAMs) have been shown to be promising approaches for tackling the energy inefficiency of deep learning and spiking neural networks. However, resistive s
Externí odkaz:
https://doaj.org/article/c2d94ee91ac64b69be730a9ee34cc93c
Publikováno v:
Neuromorphic Computing and Engineering, Vol 3, Iss 4, p 040202 (2023)
Externí odkaz:
https://doaj.org/article/18e60b54766645ee9b0462149d855b96
Autor:
Gaspard Goupy, Alexandre Juneau-Fecteau, Nikhil Garg, Ismael Balafrej, Fabien Alibart, Luc Frechette, Dominique Drouin, Yann Beilliard
Publikováno v:
Neuromorphic Computing and Engineering, Vol 3, Iss 1, p 014001 (2023)
Spiking neural networks (SNNs) are gaining attention due to their energy-efficient computing ability, making them relevant for implementation on low-power neuromorphic hardware. Their biological plausibility has permitted them to benefit from unsuper
Externí odkaz:
https://doaj.org/article/7f6d039ce24842debf21418e3b5f4caa