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A Review of Optical Neural Networks [PDF]
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 +4 more
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Optical Axons for Electro-Optical Neural Networks [PDF]
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 +4 more
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Optical neural networks: progress and challenges [PDF]
Light: Science & ApplicationsArtificial 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 +7 more
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Analysis of Diffractive Optical Neural Networks and Their Integration with Electronic Neural Networks. [PDF]
IEEE J Sel Top Quantum Electron, 2020 Optical machine learning offers advantages in terms of power efficiency, scalability and computation speed. Recently, an optical machine learning method based on Diffractive Deep Neural Networks (D2NNs) has been introduced to execute a function as the input light diffracts through passive layers, designed by deep learning using a computer.
Mengu D, Luo Y, Rivenson Y, Ozcan A.
europepmc +6 more sources
On-chip non-volatile all-optical residual neural network accelerator [PDF]
Light: Science & ApplicationsOptical neural networks (ONNs) hold substantial potential in artificial intelligence, promising faster processing speed and reduced energy consumption compared to traditional electronic neural networks, by implementing matrix operations with optical ...
Zhiqiang Quan +6 more
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Quantum nonlinearity for optical neural computing [PDF]
Nature CommunicationsThe rapid scaling of deep neural networks comes at the cost of unsustainable power consumption. While optical neural networks offer an alternative, their capabilities remain constrained by the lack of efficient optical nonlinearities. To address this, we
Qingyi Zhou +6 more
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Bio-inspired backpropagation-free training for optical neural networks [PDF]
Light: Science & ApplicationsOptical Neural Networks (ONNs) can perform matrix operations at the speed of light and are expected to bring revolutionary advances in energy-efficient computing.
Tingxuan Li +8 more
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Quantum-limited stochastic optical neural networks operating at a few quanta per activation [PDF]
Nature CommunicationsEnergy 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 +4 more
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Optical Neural Network in Free-Space and Nanophotonics
IEEE Access, 2023 The explosive data growth has resulted in increased computing costs. As Moore’s Law is increasingly slowing down, the traditional computing approach based on the von Neumann architecture is gradually becoming unable to fulfill future computing ...
Zhenlin Sun +5 more
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Optical Diffractive Convolutional Neural Networks Implemented in an All-Optical Way
Sensors, 2023 Optical neural networks can effectively address hardware constraints and parallel computing efficiency issues inherent in electronic neural networks. However, the inability to implement convolutional neural networks at the all-optical level remains a ...
Yaze Yu +4 more
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