Results 1 to 10 of about 1,604,814 (105)

A Review of Optical Neural Networks

IEEE Access, 2020
Optical neural network can process information in parallel by using the technology based on free-space and integrated platform. Over the last half century, the development of integrated circuits has been limited by Moore's law.
Xiubao Sui, Qiuhao Wu, Jia Liu, Qian Chen, Guohua Gu   +4 more
doaj   +4 more sources

Hybrid training of optical neural networks [PDF]

Optica, 2022
Optical neural networks are often trained “in-silico” on digital simulators, but physical imperfections that cannot be modelled may lead to a “reality gap” between the simulator and the physical system.
James Spall, Guo Xianxin, Alexander Lvovsky   +2 more
exaly   +2 more sources

A Review of Optical Neural Networks

Applied Sciences, 2022
With the continuous miniaturization of conventional integrated circuits, obstacles such as excessive cost, increased resistance to electronic motion, and increased energy consumption are gradually slowing down the development of electrical computing and ...
Danni Zhang, Zhongwei Tan
doaj   +2 more sources

Scientific Computing with Diffractive Optical Neural Networks [PDF]

Advanced Intelligent Systems, 2023
Diffractive optical neural networks (DONNs) are emerging as high‐throughput and energy‐efficient hardware platforms to perform all‐optical machine learning (ML) in machine vision systems.
Ruiyang Chen, Yingheng Tang, Jianzhu Ma, Weilu Gao   +3 more
doaj   +2 more sources

Efficient On-Chip Training of Optical Neural Networks Using Genetic Algorithm

ACS Photonics, 2021
: Recent advances in silicon photonic chips have made huge progress in optical computing owing to their fl exibility in the recon fi guration of various tasks.
Ai Qun Liu, Jayne Thompson
exaly   +2 more sources

Optical neural networks: progress and challenges

Light: Science & Applications
Artificial intelligence has prevailed in all trades and professions due to the assistance of big data resources, advanced algorithms, and high-performance electronic hardware.
Tingzhao Fu, Jianfa Zhang, Run Sun, Yuyao Huang, Wei Xu, Sigang Yang, Zhihong Zhu, Hongwei Chen   +7 more
doaj   +2 more sources

Quantum-limited stochastic optical neural networks operating at a few quanta per activation

Nature Communications, 2023
Energy efficiency in computation is ultimately limited by noise, with quantum limits setting the fundamental noise floor. Analog physical neural networks hold promise for improved energy efficiency compared to digital electronic neural networks. However,
Shi-Yuan Ma, Tianyu Wang, Jérémie Laydevant, Logan G. Wright, Peter L. McMahon   +4 more
doaj   +2 more sources

Design of optical neural networks with component imprecisions [PDF]

Optics Express, 2019
For the benefit of designing scalable, fault resistant optical neural networks (ONNs), we investigate the effects architectural designs have on the ONNs' robustness to imprecise components. We train two ONNs - one with a more tunable design (GridNet) and
Michael Deweese
exaly   +2 more sources

Optical Axons for Electro-Optical Neural Networks

Sensors, 2020
Recently, neuromorphic sensors, which convert analogue signals to spiking frequencies, have been reported for neurorobotics. In bio-inspired systems these sensors are connected to the main neural unit to perform post-processing of the sensor data.
Mircea Hulea, Zabih Ghassemlooy, Sujan Rajbhandari, Othman Isam Younus, Alexandru Barleanu   +4 more
doaj   +3 more sources

Class-specific differential detection in diffractive optical neural networks improves inference accuracy [PDF]

Advanced Photonics, 2019
. Optical computing provides unique opportunities in terms of parallelization, scalability, power efficiency, and computational speed and has attracted major interest for machine learning.
Jingxi Li, Yi Luo, Aydoḡan Ozcan
exaly   +2 more sources