Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Gallo, Manuel Le"'
Autor:
Momeni, Ali, Rahmani, Babak, Scellier, Benjamin, Wright, Logan G., McMahon, Peter L., Wanjura, Clara C., Li, Yuhang, Skalli, Anas, Berloff, Natalia G., Onodera, Tatsuhiro, Oguz, Ilker, Morichetti, Francesco, del Hougne, Philipp, Gallo, Manuel Le, Sebastian, Abu, Mirhoseini, Azalia, Zhang, Cheng, Marković, Danijela, Brunner, Daniel, Moser, Christophe, Gigan, Sylvain, Marquardt, Florian, Ozcan, Aydogan, Grollier, Julie, Liu, Andrea J., Psaltis, Demetri, Alù, Andrea, Fleury, Romain
Physical neural networks (PNNs) are a class of neural-like networks that leverage the properties of physical systems to perform computation. While PNNs are so far a niche research area with small-scale laboratory demonstrations, they are arguably one
Externí odkaz:
http://arxiv.org/abs/2406.03372
A Precision-Optimized Fixed-Point Near-Memory Digital Processing Unit for Analog In-Memory Computing
Autor:
Ferro, Elena, Vasilopoulos, Athanasios, Lammie, Corey, Gallo, Manuel Le, Benini, Luca, Boybat, Irem, Sebastian, Abu
Analog In-Memory Computing (AIMC) is an emerging technology for fast and energy-efficient Deep Learning (DL) inference. However, a certain amount of digital post-processing is required to deal with circuit mismatches and non-idealities associated wit
Externí odkaz:
http://arxiv.org/abs/2402.07549
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., Mackin, Charles, Gallo, Manuel Le, Chen, An, Fasoli, Andrea, Odermatt, Frederic, Li, Ning, Nandakumar, S. R., Narayanan, Pritish, Tsai, Hsinyu, Burr, Geoffrey W., Sebastian, Abu, Narayanan, Vijay
Analog in-memory computing (AIMC) -- a promising approach for energy-efficient acceleration of deep learning workloads -- computes matrix-vector multiplications (MVMs) but only approximately, due to nonidealities that often are non-deterministic or n
Externí odkaz:
http://arxiv.org/abs/2302.08469
Autor:
Zhou, Chuteng, Redondo, Fernando Garcia, Büchel, Julian, Boybat, Irem, Comas, Xavier Timoneda, Nandakumar, S. R., Das, Shidhartha, Sebastian, Abu, Gallo, Manuel Le, Whatmough, Paul N.
Always-on TinyML perception tasks in IoT applications require very high energy efficiency. Analog compute-in-memory (CiM) using non-volatile memory (NVM) promises high efficiency and also provides self-contained on-chip model storage. However, analog
Externí odkaz:
http://arxiv.org/abs/2111.06503
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:
Karunaratne, Geethan, Schmuck, Manuel, Gallo, Manuel Le, Cherubini, Giovanni, Benini, Luca, Sebastian, Abu, Rahimi, Abbas
Publikováno v:
Nature Communications volume 12, Article number: 2468 (2021)
Traditional neural networks require enormous amounts of data to build their complex mappings during a slow training procedure that hinders their abilities for relearning and adapting to new data. Memory-augmented neural networks enhance neural networ
Externí odkaz:
http://arxiv.org/abs/2010.01939
Autor:
Joshi, Vinay, Karunaratne, Geethan, Gallo, Manuel Le, Boybat, Irem, Piveteau, Christophe, Sebastian, Abu, Rajendran, Bipin, Eleftheriou, Evangelos
Deep neural networks (DNNs) have surpassed human-level accuracy in a variety of cognitive tasks but at the cost of significant memory/time requirements in DNN training. This limits their deployment in energy and memory limited applications that requi
Externí odkaz:
http://arxiv.org/abs/2003.11256
Autor:
Moraitis, Timoleon, Sebastian, Abu, Boybat, Irem, Gallo, Manuel Le, Tuma, Tomas, Eleftheriou, Evangelos
Publikováno v:
2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, 2017, pp. 1823-1830
Spiking neural networks (SNNs) could play a key role in unsupervised machine learning applications, by virtue of strengths related to learning from the fine temporal structure of event-based signals. However, some spike-timing-related strengths of SN
Externí odkaz:
http://arxiv.org/abs/1706.05563