Zobrazeno 1 - 10
of 119
pro vyhledávání: '"Maghraoui, A. el"'
Aiming to accelerate the training of large deep neural models (DNN) in an energy-efficient way, an analog in-memory computing (AIMC) accelerator emerges as a solution with immense potential. In AIMC accelerators, trainable weights are kept in memory
Externí odkaz:
http://arxiv.org/abs/2410.15155
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
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
Autor:
Maghraoui, Kauotar El, Herger, Lorraine M., Choudary, Chekuri, Tran, Kim, Deshane, Todd, Hanson, David
A composable infrastructure is defined as resources, such as compute, storage, accelerators and networking, that are shared in a pool and that can be grouped in various configurations to meet application requirements. This freedom to 'mix and match'
Externí odkaz:
http://arxiv.org/abs/2103.10911
Autor:
Benmeziane, Hadjer, Maghraoui, Kaoutar El, Ouarnoughi, Hamza, Niar, Smail, Wistuba, Martin, Wang, Naigang
Neural Architecture Search (NAS) methods have been growing in popularity. These techniques have been fundamental to automate and speed up the time consuming and error-prone process of synthesizing novel Deep Learning (DL) architectures. NAS has been
Externí odkaz:
http://arxiv.org/abs/2101.09336
Autor:
Boag, Scott, Dube, Parijat, Maghraoui, Kaoutar El, Herta, Benjamin, Hummer, Waldemar, Jayaram, K. R., Khalaf, Rania, Muthusamy, Vinod, Kalantar, Michael, Verma, Archit
Deep learning (DL), a form of machine learning, is becoming increasingly popular in several application domains. As a result, cloud-based Deep Learning as a Service (DLaaS) platforms have become an essential infrastructure in many organizations. Thes
Externí odkaz:
http://arxiv.org/abs/1805.06801