Zobrazeno 1 - 10
of 2 942
pro vyhledávání: '"Eyraud, A."'
Autor:
Ranadive, Arpit, Fazliji, Bekim, Gal, Gwenael Le, Cappelli, Giulio, Butseraen, Guilliam, Bonet, Edgar, Eyraud, Eric, Böhling, Sina, Planat, Luca, Metelmann, A., Roch, Nicolas
Superconducting traveling-wave parametric amplifiers have emerged as highly promising devices for near-quantum-limited broadband amplification of microwave signals and are essential for high quantum-efficiency microwave readout lines. Built-in isolat
Externí odkaz:
http://arxiv.org/abs/2406.19752
Autor:
Yusuf, Yusuf Oluwatoki, Dufaure, Astrid, Sorsa, Liisa-Ida, Eyraud, Christelle, Geffrin, Jean-Michel, Hérique, Alain, Pursiainen, Sampsa
This study conducts a quantitative distinguishability analysis using quasi-monostatic experimental radar data to find a topographic and backpropagated tomographic reconstruction for an analogue of asteroid Itokawa (25143). In particular, we consider
Externí odkaz:
http://arxiv.org/abs/2405.15955
Autor:
Messelot, Simon, Aparicio, Nicolas, de Seze, Elie, Eyraud, Eric, Coraux, Johann, Watanabe, Kenji, Taniguchi, Takashi, Renard, Julien
In a Josephson junction, the current phase relation relates the phase variation of the superconducting order parameter, $\varphi$, between the two superconducting leads connected through a weak link, to the dissipationless current . This relation is
Externí odkaz:
http://arxiv.org/abs/2405.13642
Publikováno v:
36th ACM Symposium on Parallelism in Algorithms and Architectures (SPAA '24), Jun 2024, Nantes, France
When designing an algorithm, one cares about arithmetic/computational complexity, but data movement (I/O) complexity plays an increasingly important role that highly impacts performance and energy consumption. For a given algorithm and a given I/O mo
Externí odkaz:
http://arxiv.org/abs/2404.16443
Autor:
Elhomsy, Victor, Planat, Luca, Niegemann, David J., Cardoso-Paz, Bruna, Badreldin, Ali, Klemt, Bernhard, Thiney, Vivien, Lethiecq, Renan, Eyraud, Eric, Dartiailh, Matthieu C., Bertrand, Benoit, Niebojewski, Heimanu, Bäuerle, Christopher, Vinet, Maud, Meunier, Tristan, Roch, Nicolas, Urdampilleta, Matias
Spins in semiconductor quantum dots hold great promise as building blocks of quantum processors. Trapping them in SiMOS transistor-like devices eases future industrial scale fabrication. Among the potentially scalable readout solutions, gate-based di
Externí odkaz:
http://arxiv.org/abs/2307.14717
We propose Rockmate to control the memory requirements when training PyTorch DNN models. Rockmate is an automatic tool that starts from the model code and generates an equivalent model, using a predefined amount of memory for activations, at the cost
Externí odkaz:
http://arxiv.org/abs/2307.01236
Autor:
Noémie Eyraud, Pierre Poupin, Marc Legrand, Agnès Caille, Anne Sauvaget, Samuel Bulteau, Bénédicte Gohier, Ghina Harika-Germaneau, Dominique Drapier, Nematollah Jaafari, Olivier Bodic, Bruno Brizard, Valérie Gissot, Catherine Belzung, Jean-Baptiste Courtine, Wissam El-Hage
Publikováno v:
Brain Stimulation, Vol 17, Iss 3, Pp 591-593 (2024)
Externí odkaz:
https://doaj.org/article/cd33cf1ed4bf4f56994b13ff035c3df9
Autor:
Anne Claire Duchez, Marco Heestermans, Charles-Antoine Arthaud, Marie-Ange Eyraud, Mailys Portier, Amélie Prier, Hind Hamzeh-Cognasse, Fabrice Cognasse
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-7 (2024)
Abstract The human population is ageing worldwide. The World Health Organization estimated that the world’s population of people aged 60 years and older will increase to at least 30%, coinciding with a growing frequency of cognitive and cardiovascu
Externí odkaz:
https://doaj.org/article/cf26ff08a3614ab49bf0085da78dcdbb
Autor:
Gusak, Julia, Cherniuk, Daria, Shilova, Alena, Katrutsa, Alexander, Bershatsky, Daniel, Zhao, Xunyi, Eyraud-Dubois, Lionel, Shlyazhko, Oleg, Dimitrov, Denis, Oseledets, Ivan, Beaumont, Olivier
Modern Deep Neural Networks (DNNs) require significant memory to store weight, activations, and other intermediate tensors during training. Hence, many models do not fit one GPU device or can be trained using only a small per-GPU batch size. This sur
Externí odkaz:
http://arxiv.org/abs/2202.10435
In this paper, we consider two fundamental symmetric kernels in linear algebra: the Cholesky factorization and the symmetric rank-$k$ update (SYRK), with the classical three nested loops algorithms for these kernels. In addition, we consider a machin
Externí odkaz:
http://arxiv.org/abs/2202.10217