Results 11 to 20 of about 263,497 (176)
Free-space optical spiking neural network
PLoS ONENeuromorphic engineering has emerged as a promising avenue for developing brain-inspired computational systems. However, conventional electronic AI-based processors often encounter challenges related to processing speed and thermal dissipation.
Reyhane Ahmadi +2 more
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Single-shot optical neural network. [PDF]
Sci Adv, 2023 Analog optical and electronic hardware has emerged as a promising alternative to digital electronics to improve the efficiency of deep neural networks (DNNs). However, previous work has been limited in scalability (input vector lengthK≈ 100 elements) or has required nonstandard DNN models and retraining, hindering widespread adoption.
Bernstein L +5 more
europepmc +4 more sources
Event-driven adaptive optical neural network. [PDF]
Sci Adv, 2023 We present an adaptive optical neural network based on a large-scale event-driven architecture. In addition to changing the synaptic weights (synaptic plasticity), the optical neural network’s structure can also be reconfigured enabling various functionalities (structural plasticity).
Brückerhoff-Plückelmann F +11 more
europepmc +4 more sources
An optical neural network using less than 1 photon per multiplication. [PDF]
Nat Commun, 2022 Wang T +5 more
europepmc +2 more sources
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. The performance of spiking neural networks has been improved using optical synapses, which offer parallel ...
Mircea Hulea +4 more
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Opto-Electronic Hybrid Network Based on Scattering Layers
Sensors, 2023 Owing to the disparity between the computing power and hardware development in electronic neural networks, optical diffraction networks have emerged as crucial technologies for various applications, including target recognition, because of their high ...
Jiakang Zhu +4 more
doaj +1 more source
Low-depth optical neural networks
Chip, 2022 Optical neural network (ONN) is emerging as an attractive proposal for machine-learning applications, enabling high-speed computation with low-energy consumption. However, there are several challenges in applying ONN for industrial applications, including the realization of activation functions and maintaining stability. In particular, the stability of
Xiao-Ming Zhang, Man-Hong Yung
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Implementation of Pruned Backpropagation Neural Network Based on Photonic Integrated Circuits
Photonics, 2021 We demonstrate a pruned high-speed and energy-efficient optical backpropagation (BP) neural network. The micro-ring resonator (MRR) banks, as the core of the weight matrix operation, are used for large-scale weighted summation.
Qi Zhang, Zhuangzhuang Xing, Duan Huang
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Optical Soliton Neural Networks
, 2023 The chapter describes the realization of photonic integrated circuits based on photorefractive solitonic waveguides. In particular, it has been shown that X-junctions formed by soliton waveguides can learn information by switching their state. X junctions can perform both supervised and unsupervised learning.
Eugenio Fazio +2 more
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Galaxy classification: deep learning on the OTELO and COSMOS databases [PDF]
, 2020 Context. The accurate classification of hundreds of thousands of galaxies observed in modern deep surveys is imperative if we want to understand the universe and its evolution. Aims.
Alfaro, Emilio J. +21 more
core +5 more sources