no code implementations • 17 Jul 2023 • Fei Xia, Kyungduk Kim, Yaniv Eliezer, Liam Shaughnessy, Sylvain Gigan, Hui Cao
Utilizing rapid optical information processing capabilities, our optical platforms could potentially offer more efficient and real-time processing solutions for a broad range of applications.
no code implementations • 21 Nov 2022 • Shiqiang Zhu, Ting Yu, Tao Xu, Hongyang Chen, Schahram Dustdar, Sylvain Gigan, Deniz Gunduz, Ekram Hossain, Yaochu Jin, Feng Lin, Bo Liu, Zhiguo Wan, Ji Zhang, Zhifeng Zhao, Wentao Zhu, Zuoning Chen, Tariq Durrani, Huaimin Wang, Jiangxing Wu, Tongyi Zhang, Yunhe Pan
In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in the era of big data, artificial intelligence and internet-of-things with new computing theories, architectures, methods, systems, and applications.
no code implementations • 10 Feb 2021 • Michael del Hougne, Sylvain Gigan, Philipp del Hougne
We demonstrate our finding in the microwave domain: harnessing the configurational DoF of a simple programmable metasurface, we localize a sub-wavelength object inside a chaotic cavity with a resolution of $\lambda/76$ using intensity-only single-frequency single-pixel measurements.
Applied Physics Signal Processing
no code implementations • 11 Dec 2020 • Julien Launay, Iacopo Poli, Kilian Müller, Gustave Pariente, Igor Carron, Laurent Daudet, Florent Krzakala, Sylvain Gigan
We present a photonic accelerator for Direct Feedback Alignment, able to compute random projections with trillions of parameters.
no code implementations • 11 Dec 2020 • Maria J. Lo Faro, Giovanna Ruello, Antonio A. Leonardi, Dario Morganti, Alessia Irrera, Francesco Priolo, Sylvain Gigan, Giorgio Volpe, Barbara Fazio
By visualizing Rayleigh scattering, photoluminescence and weakly localized Raman light from the random network of nanowires via real-space microscopy and Fourier imaging, we gain insight on the light transport mechanisms responsible for the material's inelastic coherent signal and for its directionality.
Optics Disordered Systems and Neural Networks Materials Science
no code implementations • 2 Jun 2020 • Julien Launay, Iacopo Poli, Kilian Müller, Igor Carron, Laurent Daudet, Florent Krzakala, Sylvain Gigan
As neural networks grow larger and more complex and data-hungry, training costs are skyrocketing.
1 code implementation • 30 Oct 2018 • Jonathan Dong, Florent Krzakala, Sylvain Gigan
We introduce a generalized version of phase retrieval called multiplexed phase retrieval.
no code implementations • 15 Sep 2016 • Jonathan Dong, Sylvain Gigan, Florent Krzakala, Gilles Wainrib
As a proof of concept, binary networks have been successfully trained to predict the chaotic Mackey-Glass time series.
no code implementations • 22 Oct 2015 • Alaa Saade, Francesco Caltagirone, Igor Carron, Laurent Daudet, Angélique Drémeau, Sylvain Gigan, Florent Krzakala
Random projections have proven extremely useful in many signal processing and machine learning applications.
no code implementations • 5 Oct 2015 • Boshra Rajaei, Eric W. Tramel, Sylvain Gigan, Florent Krzakala, Laurent Daudet
In this paper, the problem of compressive imaging is addressed using natural randomization by means of a multiply scattering medium.