Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Atreya Majumdar"'
Autor:
Mona Ezzadeen, Atreya Majumdar, Olivier Valorge, Niccolo Castellani, Valentin Gherman, Guillaume Regis, Bastien Giraud, Jean-Philippe Noel, Valentina Meli, Marc Bocquet, Francois Andrieu, Damien Querlioz, Jean-Michel Portal
Publikováno v:
Communications Engineering, Vol 3, Iss 1, Pp 1-15 (2024)
Abstract Resistive Random Access Memories (ReRAM) arrays provides a promising basement to deploy neural network accelerators based on near or in memory computing. However most popular accelerators rely on Ohm’s and Kirchhoff’s laws to achieve mul
Externí odkaz:
https://doaj.org/article/d5a9d55ce1b346a281c00ff8d78741b5
Autor:
Fadi Jebali, Atreya Majumdar, Clément Turck, Kamel-Eddine Harabi, Mathieu-Coumba Faye, Eloi Muhr, Jean-Pierre Walder, Oleksandr Bilousov, Amadéo Michaud, Elisa Vianello, Tifenn Hirtzlin, François Andrieu, Marc Bocquet, Stéphane Collin, Damien Querlioz, Jean-Michel Portal
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-12 (2024)
Abstract Memristor-based neural networks provide an exceptional energy-efficient platform for artificial intelligence (AI), presenting the possibility of self-powered operation when paired with energy harvesters. However, most memristor-based network
Externí odkaz:
https://doaj.org/article/9069737b51ae4f8bb963f0d34fb0f961
Autor:
Djohan Bonnet, Tifenn Hirtzlin, Atreya Majumdar, Thomas Dalgaty, Eduardo Esmanhotto, Valentina Meli, Niccolo Castellani, Simon Martin, Jean-François Nodin, Guillaume Bourgeois, Jean-Michel Portal, Damien Querlioz, Elisa Vianello
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-13 (2023)
Abstract Safety-critical sensory applications, like medical diagnosis, demand accurate decisions from limited, noisy data. Bayesian neural networks excel at such tasks, offering predictive uncertainty assessment. However, because of their probabilist
Externí odkaz:
https://doaj.org/article/71a0f04b513342a69e2eef0f95dfe8f1
Autor:
Thomas L. C. Jansen, Atreya Majumdar
Publikováno v:
JOURNAL OF PHYSICAL CHEMISTRY LETTERS, 12(23), 5512-5518. AMER CHEMICAL SOC
The Journal of Physical Chemistry Letters
The Journal of Physical Chemistry Letters
Molecular motors that exhibit controlled unidirectional rotation provide great prospects for many types of applications including nanorobotics. Existing rotational motors have two key components: photoisomerisation around a pi-bond followed by a ther
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::30583d579c76accbbe95d4c50db2c32d
https://research.rug.nl/en/publications/74635f8d-0cff-4442-8a81-d925381677b5
https://research.rug.nl/en/publications/74635f8d-0cff-4442-8a81-d925381677b5
Autor:
Etienne Nowak, Damien Querlioz, Marc Bocquet, Axel Laborieux, Atreya Majumdar, Elisa Vianello, Jean-Michel Portal, Tifenn Hirtzlin, Jacques-Olivier Klein
Publikováno v:
IEEE Transactions on Electron Devices
IEEE Transactions on Electron Devices, 2021, 68 (10), pp.4925-4932. ⟨10.1109/TED.2021.3108479⟩
IEEE Transactions on Electron Devices, Institute of Electrical and Electronics Engineers, 2021, 68 (10), pp.4925-4932. ⟨10.1109/TED.2021.3108479⟩
IEEE Transactions on Electron Devices, 2021, 68 (10), pp.4925-4932. ⟨10.1109/TED.2021.3108479⟩
IEEE Transactions on Electron Devices, Institute of Electrical and Electronics Engineers, 2021, 68 (10), pp.4925-4932. ⟨10.1109/TED.2021.3108479⟩
The implementation of current deep learning training algorithms is power-hungry, due to data transfer between memory and logic units. Oxide-based resistive random access memories (RRAMs) are outstanding candidates to implement in-memory computing, wh
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::edcbfdd9a7b73660ad52cf15830ef24e
https://hal.science/hal-03372056/document
https://hal.science/hal-03372056/document
