Reprogrammable Electro-Optic Nonlinear Activation Functions for Optical Neural Networks [PDF]
IEEE Journal of Selected Topics in Quantum Electronics, 2019 We introduce an electro-optic hardware platform for nonlinear activation functions in optical neural networks. The optical-to-optical nonlinearity operates by converting a small portion of the input optical signal into an analog electric signal, which is
Ian A. D. Williamson +5 more
semanticscholar +1 more source
Scalability of all-optical neural networks based on spatial light modulators [PDF]
Physical Review Applied, 2021 Optical implementation of artificial neural networks has been attracting great attention due to its potential in parallel computation at speed of light.
Ying Zuo +4 more
semanticscholar +1 more source
Correlating matched-filter model for analysis and optimisation of neural networks [PDF]
, 1989 A new formalism is described for modelling neural networks by means of which a clear physical understanding of the network behaviour can be gained.
Midwinter, J.E. +7 more
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Realization of optical logic gates using on-chip diffractive optical neural networks
Scientific Reports, 2022 Optical computing is highly desired as a potential strategy for circumventing the performance limitations of semiconductor-based electronic devices and circuits.
S. Zarei, A. Khavasi
semanticscholar +1 more source
Optical interconnection networks based on microring resonators [PDF]
, 2012 Optical microring resonators can be integrated on a chip to perform switching operations directly in the optical domain. Thus they become a building block to create switching elements in on-chip optical interconnection networks, which promise to overcome
Cuda, Davide +6 more
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Performance analysis of different DCNN models in remote sensing image object detection
EURASIP Journal on Image and Video Processing, 2022 In recent years, deep learning, especially deep convolutional neural networks (DCNN), has made great progress. Many researchers use different DCNN models to detect remote sensing targets. Different DCNN models have different advantages and disadvantages.
Huaijin Liu +3 more
doaj +1 more source
Analyzing Echo-state Networks Using Fractal Dimension [PDF]
, 2022 This work joins aspects of reservoir optimization, information-theoretic optimal encoding, and at its center fractal analysis. We build on the observation that, due to the recursive nature of recurrent neural networks, input sequences appear as fractal ...
Obst, Oliver +3 more
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Analyzing the Sensitivity of Deep Neural Networks for Sentiment Analysis: A Scoring Approach
, 2020 Part of IEEE WCCI 2020 is the world’s largest technical event on computational intelligence, featuring the three flagship conferences of the IEEE Computational Intelligence Society (CIS) under one roof: The 2020 International Joint Conference on Neural ...
Wei Emma Zhang +7 more
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Large-Scale Optical Neural Networks based on Photoelectric Multiplication [PDF]
Physical Review X, 2018 Recent success in deep neural networks has generated strong interest in hardware accelerators to improve speed and energy consumption. This paper presents a new type of photonic accelerator based on coherent detection that is scalable to large ($N ...
R. Hamerly +4 more
semanticscholar +1 more source
A fibre optic sensor for the measurement of surface roughness and displacement using artificial neural networks [PDF]
, 1997 This paper presents a fiber optic sensor system, artificial neural networks (fast back-propagation) are employed for the data processing. The use of the neural networks makes it possible for the sensor to be used both for surface roughness and ...
Butler, C, Lu, Y, Zhang, K, Yang, QP
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