Ultrasensitive, Ultrafast, and Gate-Tunable Two-Dimensional Photodetectors in Ternary Rhombohedral ZnIn2S4 for Optical Neural Networks. [PDF]
ACS Applied Materials and Interfaces, 2022 The demand for high-performance semiconductors in electronics and optoelectronics has prompted the expansion of low-dimensional materials research to ternary compounds.
W. Zhen +4 more
semanticscholar +1 more source
Free-Space Optical Neural Network Based on Optical Nonlinearity and Pooling Operations
IEEE Access, 2021 Despite various optical realizations of convolutional neural networks (CNNs), optical implementation of nonlinear activation functions and pooling operations are still challenging problems.
Hoda Sadeghzadeh +2 more
doaj +1 more source
Bit error performance of diffuse indoor optical wireless channel pulse position modulation system employing artificial neural networks for channel equalisation [PDF]
, 2009 The bit-error rate (BER) performance of a pulse position modulation (PPM) scheme for non-line-of-sight indoor optical links employing channel equalisation based on the artificial neural network (ANN) is reported.
Ghassemlooy, Zabih +2 more
core +1 more source
Sensors, 2022 In the last years, materializations of neuromorphic circuits based on nanophotonic arrangements have been proposed, which contain complete optical circuits, laser, photodetectors, photonic crystals, optical fibers, flat waveguides and other passive ...
Konstantinos Demertzis +3 more
doaj +1 more source
Reconfigurable Activation Functions in Integrated Optical Neural Networks
IEEE Journal of Selected Topics in Quantum Electronics, 2022 The implementation of nonlinear activation functions is one of the key challenges that optical neural networks face. To the date, different approaches have been proposed, including switching to digital implementations, electro-optical or all optical.
José Roberto Rausell Campo +1 more
semanticscholar +1 more source
Lazy training of radial basis neural networks [PDF]
, 2006 Proceeding of: 16th International Conference on Artificial Neural Networks, ICANN 2006. Athens, Greece, September 10-14, 2006Usually, training data are not evenly distributed in the input space.
Galván, Inés M. +5 more
core +1 more source
Training large-scale optoelectronic neural networks with dual-neuron optical-artificial learning
Nature Communications, 2023 Optoelectronic neural networks (ONN) are a promising avenue in AI computing due to their potential for parallelization, power efficiency, and speed.
Xiaoyun Yuan +4 more
doaj +1 more source
Parity-time Symmetric Optical Neural Networks [PDF]
Conference on Lasers and Electro-Optics, 2021 An optical neural network architecture is proposed that utilizes parity-time symmetric couplers as its building blocks. Gain–loss contrasts across the array are adjusted as a means to train the network.
Haoqin Deng, M. Khajavikhan
semanticscholar +1 more source
Evolutionary cellular configurations for designing feed-forward neural networks architectures [PDF]
, 2001 Proceeding of: 6th International Work-Conference on Artificial and Natural Neural Networks, IWANN 2001 Granada, Spain, June 13–15, 2001In the recent years, the interest to develop automatic methods to determine appropriate architectures of feed-forward ...
Gutiérrez Sánchez, Germán +6 more
core +1 more source
Digital-analog hybrid matrix multiplication processor for optical neural networks [PDF]
Nature CommunicationsOptical neural networks (ONNs) promise computing efficiency beyond microelectronics for modern artificial intelligence (AI). Current ONNs using analog matrix-vector multiplication (MVM) implementations are fundamentally limited in numerical precision due
Xiansong Meng +7 more
semanticscholar +1 more source