Neural network experiment demonstrating all-optical data switching

1998 ◽  
Author(s):  
Evert C. Mos ◽  
H. de Waardt ◽  
J. J. H. B. Schleipen
Keyword(s):  
Neural Network ◽  
Optical Data ◽  
All Optical ◽  
Network Experiment ◽  
Data Switching

All-optical data switching in an optical-fiber link using a GaAs optical bistable device

1984 ◽  
Vol 9 (7) ◽  
pp. 297 ◽  
Author(s):  
T. Venkatesan ◽  
P. J. Lemaire ◽  
B. Wilkens ◽  
L. Soto ◽  
J. L. Jewell ◽  
...  
Keyword(s):  
Optical Fiber ◽  
Optical Data ◽  
All Optical ◽  
Fiber Link ◽  
Bistable Device ◽  
Data Switching

SiGeC Waveguide for All-Optical Data Switching

ACS Photonics ◽  
2018 ◽  
Vol 5 (6) ◽  
pp. 2251-2260 ◽  
Author(s):  
Bo-Ji Huang ◽  
Cheng-Ting Tsai ◽  
Yung-Hsiang Lin ◽  
Chih-Hsien Cheng ◽  
Huai-Yung Wang ◽  
...  
Keyword(s):  
Optical Data ◽  
All Optical ◽  
Data Switching

scholarly journals Freely scalable and reconfigurable optical hardware for deep learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Liane Bernstein ◽  
Alexander Sludds ◽  
Ryan Hamerly ◽  
Vivienne Sze ◽  
Joel Emer ◽  
...  
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Data Transfer ◽  
Power Delivery ◽  
Optical Data ◽  
Optical Multicast ◽  
Optical Neural Network ◽  
Fully Connected ◽  
Management Power

AbstractAs deep neural network (DNN) models grow ever-larger, they can achieve higher accuracy and solve more complex problems. This trend has been enabled by an increase in available compute power; however, efforts to continue to scale electronic processors are impeded by the costs of communication, thermal management, power delivery and clocking. To improve scalability, we propose a digital optical neural network (DONN) with intralayer optical interconnects and reconfigurable input values. The path-length-independence of optical energy consumption enables information locality between a transmitter and a large number of arbitrarily arranged receivers, which allows greater flexibility in architecture design to circumvent scaling limitations. In a proof-of-concept experiment, we demonstrate optical multicast in the classification of 500 MNIST images with a 3-layer, fully-connected network. We also analyze the energy consumption of the DONN and find that digital optical data transfer is beneficial over electronics when the spacing of computational units is on the order of $$>10\,\upmu $$ > 10 μ m.

All-optical Sudoku solver with photonic spiking neural network

2021 ◽  
pp. 127068
Author(s):  
Shuang Gao ◽  
Shuiying Xiang ◽  
Ziwei Song ◽  
Yanan Han ◽  
Yue Hao
Keyword(s):  
Neural Network ◽  
Spiking Neural Network ◽  
All Optical

All optical NAND/NOR and majority gates using nonlinear photonic crystal ring resonator

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Hassan Mamnoon-Sofiani ◽  
Sahel Javahernia
Keyword(s):  
Photonic Crystal ◽  
Ring Resonator ◽  
Logic Gates ◽  
Building Blocks ◽  
Optical Data ◽  
Optical Logic ◽  
Optical Logic Gates ◽  
Nonlinear Photonic Crystal ◽  
All Optical ◽  
Photonic Crystal Ring Resonator

Abstract All optical logic gates are building blocks for all optical data processors. One way of designing optical logic gates is using threshold switching which can be realized by combining an optical resonator with nonlinear Kerr effect. In this paper we showed that a novel structure consisting of nonlinear photonic crystal ring resonator which can be used for realizing optical NAND/NOR and majority gates. The delay time of the proposed NAND/NOR and majority gates are 2.5 ps and 1.5 ps respectively. Finite difference time domain and plane wave expansion methods were used for simulating the proposed optical logic gates. The total footprint of the proposed structure is about 988 μm2.

scholarly journals Cloud Removal with Fusion of High Resolution Optical and SAR Images Using Generative Adversarial Networks

2020 ◽  
Vol 12 (1) ◽  
pp. 191 ◽  
Author(s):  
Jianhao Gao ◽  
Qiangqiang Yuan ◽  
Jie Li ◽  
Hai Zhang ◽  
Xin Su
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Optical Image ◽  
Optical Data ◽  
Generative Adversarial Networks ◽  
Second Step ◽  
Missing Information ◽  
Sar Images ◽  
Generative Adversarial Network ◽  
Cloud Removal

The existence of clouds is one of the main factors that contributes to missing information in optical remote sensing images, restricting their further applications for Earth observation, so how to reconstruct the missing information caused by clouds is of great concern. Inspired by the image-to-image translation work based on convolutional neural network model and the heterogeneous information fusion thought, we propose a novel cloud removal method in this paper. The approach can be roughly divided into two steps: in the first step, a specially designed convolutional neural network (CNN) translates the synthetic aperture radar (SAR) images into simulated optical images in an object-to-object manner; in the second step, the simulated optical image, together with the SAR image and the optical image corrupted by clouds, is fused to reconstruct the corrupted area by a generative adversarial network (GAN) with a particular loss function. Between the first step and the second step, the contrast and luminance of the simulated optical image are randomly altered to make the model more robust. Two simulation experiments and one real-data experiment are conducted to confirm the effectiveness of the proposed method on Sentinel 1/2, GF 2/3 and airborne SAR/optical data. The results demonstrate that the proposed method outperforms state-of-the-art algorithms that also employ SAR images as auxiliary data.

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