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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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Low-depth optical neural networks
Chip, 2022 Optical neural network (ONNs) are emerging as attractive proposals for machine-learning applications. However, the stability of ONNs decreases with the circuit depth, limiting the scalability of ONNs for practical uses.
Xiao-Ming Zhang, Man-Hong Yung
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SPIE Proceedings, 1994 The trade-off between the number of neurons that can be implemented with a single correlator and the shift invariance that each neuron has is investigated.
Levene, Michael, Psaltis, Demetri
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Optical multilayer neural networks [PDF]
SPIE Proceedings, 1991 In order to implement fully adaptive optical multilayer neural networks, a number of issues involving both learning algorithms and device technologies need to be addressed.
Psaltis, Demetri, Qiao, Yong
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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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Genetically programmable optical random neural networks
Communications PhysicsToday, machine learning tools, particularly artificial neural networks, have become crucial for diverse applications. However, current digital computing tools to train and deploy artificial neural networks often struggle with massive data sizes and high ...
Bora Çarpınlıoğlu, Uğur Teğin
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Compressing and expanding optical matrix-vector multipliers for enabling optical image encoder-decoders and generators [PDF]
Light: Science & ApplicationsBoth compressing and expanding optical matrix-vector multipliers are necessary for the full optical realization of neural networks. An expanding multiplier scheme is proposed, which, together with common compressing multipliers, is employed to ...
Adrian Stern
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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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