Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group
Conferences > CLEO_FS > 2023 > JW2A > JW2A.77

Design and Model of On-Chip Metalens for Silicon Photonics Convolutional Neural Network

Nicola Peserico, Hangbo Yang, Xiaoxuan Ma, Shurui Li, Jonathan K. George, Puneet Gupta, Chee Wei Wong, and Volker J. Sorger

Not Accessible

Your library or personal account may give you access

Abstract

Convolution Neural Networks are one of the most diffuse Neural Networks. Here, we present a Silicon photonic CNN chip that leverages hardware complexity by exploiting Fourier light properties to perform convolution operations, focusing on the chip- based Fourier-lens.

© 2023 The Author(s)

PDF Article
More Like This
Design and Testing of Integrated Photonic Chip for Convolution Neural Network

Nicola Peserico, Hangbo Yang, Xiaoxuan Ma, Shurui Li, Mostafa Hosseini, Jonathan K. George, Puneet Gupta, Chee Wei Wong, and Volker J. Sorger
ITh3D.7 Imaging Systems and Applications (IS) 2022

Design and Testing of Integrated 4F System into Silicon Photonics Chip for Convolutional Neural Network

Nicola Peserico, Hangbo Yang, Xiaoxuan Ma, Shurui Li, Mostafa Hosseini, Jonathan K. George, Puneet Gupta, Chee Wei Wong, and Volker J. Sorger
IM4B.5 Integrated Photonics Research, Silicon and Nanophotonics (IPR) 2022

Investigating and Scale-up of Programmable On-chip Photonic Convolution Neural Networks Based on Joint Transform Correlator

Hangbo Yang, Shurui Li, Nicola Peserico, Xiaoxuan Ma, Mostafa Hosseini, Jonathan K. George, Puneet Gupta, Volker J. Sorger, and Chee Wei Wong
SM2P.5 CLEO: Science and Innovations (CLEO:S&I) 2023

View More...

Poster Presentation

Media 1: PDF (2042 KB)     
Select as filters
Select Topics Cancel
Top
       
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.
Privacy | Terms of Use