neural network hardware
Recently Published Documents


TOTAL DOCUMENTS

119
(FIVE YEARS 36)

H-INDEX

10
(FIVE YEARS 3)

2022 ◽  
Vol 6 (1) ◽  
Author(s):  
Marco Rossi ◽  
Sofia Vallecorsa

AbstractIn this work, we investigate different machine learning-based strategies for denoising raw simulation data from the ProtoDUNE experiment. The ProtoDUNE detector is hosted by CERN and it aims to test and calibrate the technologies for DUNE, a forthcoming experiment in neutrino physics. The reconstruction workchain consists of converting digital detector signals into physical high-level quantities. We address the first step in reconstruction, namely raw data denoising, leveraging deep learning algorithms. We design two architectures based on graph neural networks, aiming to enhance the receptive field of basic convolutional neural networks. We benchmark this approach against traditional algorithms implemented by the DUNE collaboration. We test the capabilities of graph neural network hardware accelerator setups to speed up training and inference processes.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ádám Papp ◽  
Wolfgang Porod ◽  
Gyorgy Csaba

AbstractWe demonstrate the design of a neural network hardware, where all neuromorphic computing functions, including signal routing and nonlinear activation are performed by spin-wave propagation and interference. Weights and interconnections of the network are realized by a magnetic-field pattern that is applied on the spin-wave propagating substrate and scatters the spin waves. The interference of the scattered waves creates a mapping between the wave sources and detectors. Training the neural network is equivalent to finding the field pattern that realizes the desired input-output mapping. A custom-built micromagnetic solver, based on the Pytorch machine learning framework, is used to inverse-design the scatterer. We show that the behavior of spin waves transitions from linear to nonlinear interference at high intensities and that its computational power greatly increases in the nonlinear regime. We envision small-scale, compact and low-power neural networks that perform their entire function in the spin-wave domain.


2021 ◽  
Author(s):  
Ahmed J. Abd El-Maksoud ◽  
Abdallah Mohamed ◽  
Ahmed Tarek ◽  
Amr Adel ◽  
Amr Eid ◽  
...  

Author(s):  
Joel Minguet Lopez ◽  
Tifenn HIRTZLIN ◽  
Manon Dampfhoffer ◽  
Laurent Grenouillet ◽  
Lucas Reganaz ◽  
...  

2021 ◽  
pp. 2103376 ◽  
Author(s):  
Sifan Li ◽  
Mei‐Er Pam ◽  
Yesheng Li ◽  
Li Chen ◽  
Yu‐Chieh Chien ◽  
...  

Author(s):  
Hendrik Wohrle ◽  
Mariela De Lucas Alvarez ◽  
Fabian Schlenke ◽  
Alexander Walsemann ◽  
Michael Karagounis ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document