A survey of approaches for implementing optical neural networks

Xu, Rongkun; Lv, Pin; Fei, Xu; Ma, Rui

doi:10.1016/j.optlastec.2020.106787

Cited by 40 publications

(13 citation statements)

References 97 publications

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“…Lately, there is a rising attention towards utilizing optical computing [55,[111][112][113] to carry out specific machine learning tasks, thanks to the inherent advantages of nanooptics like parallel computation, low power consumption and propagating at the speed of light. Among the proposed optical hardware schemes, the so-named artificial intelligence interference [52] may offer opportunities to establish photonic systems for visual computing applications.…”

Section: Optical Interference Unitmentioning

confidence: 99%

Intelligent designs in nanophotonics: from optimization towards inverse creation

Wang

Yan

et al. 2021

PhotoniX

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Applying intelligence algorithms to conceive nanoscale meta-devices becomes a flourishing and extremely active scientific topic over the past few years. Inverse design of functional nanostructures is at the heart of this topic, in which artificial intelligence (AI) furnishes various optimization toolboxes to speed up prototyping of photonic layouts with enhanced performance. In this review, we offer a systemic view on recent advancements in nanophotonic components designed by intelligence algorithms, manifesting a development trend from performance optimizations towards inverse creations of novel designs. To illustrate interplays between two fields, AI and photonics, we take meta-atom spectral manipulation as a case study to introduce algorithm operational principles, and subsequently review their manifold usages among a set of popular meta-elements. As arranged from levels of individual optimized piece to practical system, we discuss algorithm-assisted nanophotonic designs to examine their mutual benefits. We further comment on a set of open questions including reasonable applications of advanced algorithms, expensive data issue, and algorithm benchmarking, etc. Overall, we envision mounting photonic-targeted methodologies to substantially push forward functional artificial meta-devices to profit both fields.

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Section: Optical Interference Unitmentioning

confidence: 99%

Intelligent designs in nanophotonics: from optimization towards inverse creation

Wang

Yan

et al. 2021

PhotoniX

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“…Other ML algorithms have also been used to predict RSSI for a hybrid RF/FSO detector by developing both regression and classification models [38,39]. Finally, an overview of the, as expected, next generation of the ANN, the so-called optical neural networks (ONNs) and previous studies on the field are reviewed in [40]. The novelty of the current study lies in the comparison of various classical ML algorithms that are presented for the first time in predicting RSSI measurements, especially for the particular domain of interest, Piraeus, Greece.…”

Section: Machine Learning Based Fso Research Backgroundmentioning

confidence: 99%

“…neural networks (ONNs) and previous studies on the field are reviewed in [40]. The novelty of the current study lies in the comparison of various classical ML algorithms that are presented for the first time in predicting RSSI measurements, especially for the particular domain of interest, Piraeus, Greece.…”

Section: Measurement Systems Overviewmentioning

confidence: 99%

Using Machine Learning Algorithms for Accurate Received Optical Power Prediction of an FSO Link over a Maritime Environment

et al. 2021

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The performance prediction of an optical communications link over maritime environments has been extensively researched over the last two decades. The various atmospheric phenomena and turbulence effects have been thoroughly explored, and long-term measurements have allowed for the construction of simple empirical models. The aim of this work is to demonstrate the prediction accuracy of various machine learning (ML) algorithms for a free-space optical communication (FSO) link performance, with respect to real time, non-linear atmospheric conditions. A large data set of received signal strength indicators (RSSI) for a laser communications link has been collected and analyzed against seven local atmospheric parameters (i.e., wind speed, pressure, temperature, humidity, dew point, solar flux and air-sea temperature difference). The k-nearest-neighbors (KNN), tree-based methods-decision trees, random forest and gradient boosting- and artificial neural networks (ANN) have been employed and compared among each other using the root mean square error (RMSE) and the coefficient of determination (R2) of each model as the primary performance indices. The regression analysis revealed an excellent fit for all ML models, indicative of their ability to offer a significant improvement in FSO performance modeling as compared to traditional regression models. The best-performing R2 model found to be the ANN approach (0.94867), while random forests achieved the most optimal RMSE result (7.37).

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“…Photonics offers strong parallelization capabilities and a plethora of commercialgrade and potentially integrable tools to manipulate light degrees of freedom; hence, this platform is considered a good candidate for the implementation of fast non-electronic neural networks [6]. Different schemes for photonic implementations of randomized neural networks have already been explored.…”

Section: Introductionmentioning

confidence: 99%

Parallel Extreme Learning Machines Based on Frequency Multiplexing

Lupo

Massar

2021

Applied Sciences

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In a recent work, we reported on an Extreme Learning Machine (ELM) implemented in a photonic system based on frequency multiplexing, where each wavelength of the light encodes a different neuron state. In the present work, we experimentally demonstrate the parallelization potentialities of this approach. We show that multiple frequency combs centered on different frequencies can copropagate in the same system, resulting in either multiple independent ELMs executed in parallel on the same substrate or a single ELM with an increased number of neurons. We experimentally tested the performances of both these operation modes on several classification tasks, employing up to three different light sources, each of which generates an independent frequency comb. We also numerically evaluated the performances of the system in configurations containing up to 15 different light sources.

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A survey of approaches for implementing optical neural networks

Cited by 40 publications

References 97 publications

Intelligent designs in nanophotonics: from optimization towards inverse creation

Intelligent designs in nanophotonics: from optimization towards inverse creation

Using Machine Learning Algorithms for Accurate Received Optical Power Prediction of an FSO Link over a Maritime Environment

Parallel Extreme Learning Machines Based on Frequency Multiplexing

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