Zobrazeno 1 - 10
of 1 520
pro vyhledávání: '"A. Maghraoui"'
Autor:
R. Audran, H. Chtioui, A. C. Thierry, C. E. Mayor, L. Vallotton, K. Dao, L. E. Rothuizen, A. Maghraoui, E. J. Pennella, F. Brunner-Ferber, T. Buclin, F. Spertini
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-12 (2024)
Abstract The initial Phase-I single centre, single dose, randomized, double-blind, cross-over study was planned to assess the pharmacokinetic and pharmacodynamic bioequivalence of the trastuzumab biosimilar (MYL-1401O) compared to the reference Herce
Externí odkaz:
https://doaj.org/article/5a559ec5f6b549d9beb99f07224d0849
Autor:
Chowdhury, Mohammed Nowaz Rabbani, Wang, Meng, Maghraoui, Kaoutar El, Wang, Naigang, Chen, Pin-Yu, Carothers, Christopher
Publikováno v:
The 41st International Conference on Machine Learning, ICML 2024
The sparsely gated mixture of experts (MoE) architecture sends different inputs to different subnetworks, i.e., experts, through trainable routers. MoE reduces the training computation significantly for large models, but its deployment can be still m
Externí odkaz:
http://arxiv.org/abs/2405.16646
This work investigates the role of the emerging Analog In-memory computing (AIMC) paradigm in enabling Medical AI analysis and improving the certainty of these models at the edge. It contrasts AIMC's efficiency with traditional digital computing's li
Externí odkaz:
http://arxiv.org/abs/2403.08796
Hardware-aware Neural Architecture Search (HW-NAS) is increasingly being used to design efficient deep learning architectures. An efficient and flexible search space is crucial to the success of HW-NAS. Current approaches focus on designing a macro-a
Externí odkaz:
http://arxiv.org/abs/2309.11246
Autor:
Gallo, Manuel Le, Lammie, Corey, Buechel, Julian, Carta, Fabio, Fagbohungbe, Omobayode, Mackin, Charles, Tsai, Hsinyu, Narayanan, Vijay, Sebastian, Abu, Maghraoui, Kaoutar El, Rasch, Malte J.
Publikováno v:
APL Machine Learning (2023) 1 (4): 041102
Analog In-Memory Computing (AIMC) is a promising approach to reduce the latency and energy consumption of Deep Neural Network (DNN) inference and training. However, the noisy and non-linear device characteristics, and the non-ideal peripheral circuit
Externí odkaz:
http://arxiv.org/abs/2307.09357
Autor:
Benmeziane, Hadjer, Lammie, Corey, Boybat, Irem, Rasch, Malte, Gallo, Manuel Le, Tsai, Hsinyu, Muralidhar, Ramachandran, Niar, Smail, Hamza, Ouarnoughi, Narayanan, Vijay, Sebastian, Abu, Maghraoui, Kaoutar El
The advancement of Deep Learning (DL) is driven by efficient Deep Neural Network (DNN) design and new hardware accelerators. Current DNN design is primarily tailored for general-purpose use and deployment on commercially viable platforms. Inference a
Externí odkaz:
http://arxiv.org/abs/2305.10459
We report about element specific measurements of ultrafast demagnetization and magnetization precession damping in Permalloy (Py) thin films. Magnetization dynamics induced by optical pump at $1.5$eV is probed simultaneously at the $M_{2,3}$ edges of
Externí odkaz:
http://arxiv.org/abs/2303.15837
Autor:
El Maghraoui, Adila, El Hadraoui, Hicham, Ledmaoui, Younes, El Bazi, Nabil, Guennouni, Nasr, Chebak, Ahmed
Publikováno v:
In Sustainable Energy, Grids and Networks September 2024 39
Publikováno v:
In Future Generation Computer Systems August 2024 157:29-40
Autor:
Rasch, Malte J., Moreda, Diego, Gokmen, Tayfun, Gallo, Manuel Le, Carta, Fabio, Goldberg, Cindy, Maghraoui, Kaoutar El, Sebastian, Abu, Narayanan, Vijay
We introduce the IBM Analog Hardware Acceleration Kit, a new and first of a kind open source toolkit to simulate analog crossbar arrays in a convenient fashion from within PyTorch (freely available at https://github.com/IBM/aihwkit). The toolkit is u
Externí odkaz:
http://arxiv.org/abs/2104.02184