Zobrazeno 1 - 10
of 530
pro vyhledávání: '"Bienstman, Peter"'
Autor:
Marchesin, Federico, Hejda, Matěj, Carmona, Tzamn Melendez, Di Carlo, Stefano, Savino, Alessandro, Pavanello, Fabio, Van Vaerenbergh, Thomas, Bienstman, Peter
Matrix-vector multiplications (MVMs) are essential for a wide range of applications, particularly in modern machine learning and quantum computing. In photonics, there is growing interest in developing architectures capable of performing linear opera
Externí odkaz:
http://arxiv.org/abs/2411.02243
Autor:
Biasi, Stefano, Foradori, Alessandro, Franchi, Riccardo, Lugnan, Alessio, Bienstman, Peter, Pavesi, Lorenzo
While biological neurons ensure unidirectional signalling, scalable integrated photonic neurons, such as silicon microresonators, respond the same way regardless of excitation direction due to the Lorentz reciprocity principle. Here, we show that a n
Externí odkaz:
http://arxiv.org/abs/2410.03257
Autor:
Hejda, Matěj, Marchesin, Federico, Papadimitriou, George, Gizopoulos, Dimitris, Charbonnier, Benoit, Orobtchouk, Régis, Bienstman, Peter, Van Vaerenbergh, Thomas, Pavanello, Fabio
In this work, we discuss our vision for neuromorphic accelerators based on integrated photonics within the framework of the Horizon Europe NEUROPULS project. Augmented integrated photonic architectures that leverage phase-change and III-V materials f
Externí odkaz:
http://arxiv.org/abs/2407.06240
Autor:
Lugnan, Alessio, Aggarwal, Samarth, Brückerhoff-Plückelmann, Frank, Wright, C. David, Pernice, Wolfram H. P., Bhaskaran, Harish, Bienstman, Peter
Plastic self-adaptation, nonlinear recurrent dynamics and multi-scale memory are desired features in hardware implementations of neural networks, because they enable them to learn, adapt and process information similarly to the way biological brains
Externí odkaz:
http://arxiv.org/abs/2312.03802
Autor:
Ma, Chonghuai, Van Kerrebrouck, Joris, Deng, Hong, Sackesyn, Stijn, Gooskens, Emmanuel, Bai, Bing, Dambre, Joni, Bienstman, Peter
Integrated photonic reservoir computing has been demonstrated to be able to tackle different problems because of its neural network nature. A key advantage of photonic reservoir computing over other neuromorphic paradigms is its straightforward reado
Externí odkaz:
http://arxiv.org/abs/2306.15845
Over the last decade, researchers have studied the synergy between quantum computing (QC) and classical machine learning (ML) algorithms. However, measurements in QC often disturb or destroy quantum states, requiring multiple repetitions of data proc
Externí odkaz:
http://arxiv.org/abs/2306.00134
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:
Pavanello, Fabio, Vatajelu, Elena Ioana, Bosio, Alberto, Van Vaerenbergh, Thomas, Bienstman, Peter, Charbonnier, Benoit, Carpegna, Alessio, Di Carlo, Stefano, Savino, Alessandro
Publikováno v:
2023 IEEE 41st VLSI Test Symposium (VTS)
The field of neuromorphic computing has been rapidly evolving in recent years, with an increasing focus on hardware design and reliability. This special session paper provides an overview of the recent developments in neuromorphic computing, focusing
Externí odkaz:
http://arxiv.org/abs/2305.01818
Imaging flow cytometry systems aim to analyze a huge number of cells or micro-particles based on their physical characteristics. The vast majority of current systems acquire a large amount of images which are used to train deep artificial neural netw
Externí odkaz:
http://arxiv.org/abs/2303.10632
Photonic reservoir computing is a promising candidate for low-energy computing at high bandwidths. Despite recent successes, there are bounds to what one can achieve simply by making photonic reservoirs larger. Therefore, a switch from single-reservo
Externí odkaz:
http://arxiv.org/abs/1910.13332