Zobrazeno 1 - 10
of 597
pro vyhledávání: '"O'Connor, Ian"'
Autor:
de Queiroz, Mauricio Gomes, Jimenez, Paul, Cardoso, Raphael, Costa, Mateus Vidaletti, Abdalla, Mohab, O'Connor, Ian, Bosio, Alberto, Pavanello, Fabio
Publikováno v:
APL Mach. Learn. 1 September 2024; 2 (3): 036109
Photonic Neural Networks (PNNs) are gaining significant interest in the research community due to their potential for high parallelization, low latency, and energy efficiency. PNNs compute using light, which leads to several differences in implementa
Externí odkaz:
http://arxiv.org/abs/2406.18757
Autor:
Shahroodi, Taha, Cardoso, Raphael, Wong, Stephan, Bosio, Alberto, O'Connor, Ian, Hamdioui, Said
State-of-the-Art (SotA) hardware implementations of Deep Neural Networks (DNNs) incur high latencies and costs. Binary Neural Networks (BNNs) are potential alternative solutions to realize faster implementations without losing accuracy. In this paper
Externí odkaz:
http://arxiv.org/abs/2401.17724
Autor:
O'Connor, Ian, Cantan, Mayeul, Marchand, Cédric, Vilquin, Bertrand, Slesazeck, Stefan, Breyer, Evelyn T., Mulaosmanovic, Halid, Mikolajick, Thomas, Giraud, Bastien, Noël, Jean-Philippe, Ionescu, Adrian, Igor, Igor
Edge computing requires highly energy efficient microprocessor units with embedded non-volatile memories to process data at IoT sensor nodes. Ferroelectric non-volatile memory devices are fast, low power and high endurance, and could greatly enhance
Externí odkaz:
https://tud.qucosa.de/id/qucosa%3A76920
https://tud.qucosa.de/api/qucosa%3A76920/attachment/ATT-0/
https://tud.qucosa.de/api/qucosa%3A76920/attachment/ATT-0/
Autor:
Aziz, Ahmedullah, Breyer, Evelyn T., Chen, An, Chen, Xiaoming, Datta, Suman, Gupta, Sumeet Kumar, Hoffmann, Michael, Hu, Xiaobo Sharon, Ionescu, Adrian, Jerry, Matthew, Mikolajick, Thomas, Mulaosmanovic, Halid, Ni, Kai, Niemier, Michael, O'Connor, Ian, Saha, Atanu, Slesazeck, Stefan, Thirumala, Sandeep Krishna, Yin, Xunzhao
In this paper, we consider devices, circuits, and systems comprised of transistors with integrated ferroelectrics. Said structures are actively being considered by various semiconductor manufacturers as they can address a large and unique design spac
Externí odkaz:
https://tud.qucosa.de/id/qucosa%3A76838
https://tud.qucosa.de/api/qucosa%3A76838/attachment/ATT-0/
https://tud.qucosa.de/api/qucosa%3A76838/attachment/ATT-0/
Autor:
Ram, Ankita, Maity, Krishna, Marchand, Cédric, Mahmoudi, Aymen, Kshirsagar, Aseem Rajan, Soliman, Mohamed, Taniguchi, Takashi, Watanabe, Kenji, Doudin, Bernard, Ouerghi, Abdelkarim, Reichardt, Sven, O'Connor, Ian, Dayen, Jean-Francois
In this work, we demonstrate the suitability of Reconfigurable Ferroelectric Field-Effect- Transistors (Re-FeFET) for designing non-volatile reconfigurable logic-in-memory circuits with multifunctional capabilities. Modulation of the energy landscape
Externí odkaz:
http://arxiv.org/abs/2310.14648
Autor:
Pavanello, Fabio, Marchand, Cedric, O'Connor, Ian, Orobtchouk, Regis, Mandorlo, Fabien, Letartre, Xavier, Cueff, Sebastien, Vatajelu, Elena Ioana, Di Natale, Giorgio, Cluzel, Benoit, Coillet, Aurelien, Charbonnier, Benoit, Noe, Pierre, Kavan, Frantisek, Zoldak, Martin, Szaj, Michal, Bienstman, Peter, Van Vaerenbergh, Thomas, Ruhrmair, Ulrich, Flores, Paulo, Silva, Luis Guerra e, Chaves, Ricardo, Silveira, Luis-Miguel, Ceccato, Mariano, Gizopoulos, Dimitris, Papadimitriou, George, Karakostas, Vasileios, Brando, Axel, Cazorla, Francisco J., Canal, Ramon, Closas, Pau, Gusi-Amigo, Adria, Crovetti, Paolo, Carpegna, Alessio, Carmona, Tzamn Melendez, Di Carlo, Stefano, Savino, Alessandro
Publikováno v:
IEEE European Test Symposium 2023
This special session paper introduces the Horizon Europe NEUROPULS project, which targets the development of secure and energy-efficient RISC-V interfaced neuromorphic accelerators using augmented silicon photonics technology. Our approach aims to de
Externí odkaz:
http://arxiv.org/abs/2305.03139
Autor:
Dupuis, Etienne, Filip, Silviu-Ioan, Sentieys, Olivier, Novo, David, O'Connor, Ian, Bosio, Alberto
The design and implementation of Deep Learning (DL) models is currently receiving a lot of attention from both industrials and academics. However, the computational workload associated with DL is often out of reach for low-power embedded devices and
Externí odkaz:
http://arxiv.org/abs/2212.04297