Electrooptic Nonlinear Activation Functions for Vector Matrix Multiplications in Optical Neural Networks

George, Jonathan; Amin, Rubab; Mehrabian, Armin; Khurgin, Jacob B.; El‐Ghazawi, Tarek; Prucnal, Paul R.; Sorger, Volker J.

doi:10.1364/sppcom.2018.spw4g.3

Cited by 14 publications

(10 citation statements)

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“…optical implementations of various neural network architectures [4]- [10], with a recent resurgence [11]- [22], following the availability of powerful new tools for applying deep neural networks [23], [24], which have redefined the state-of-the-art for a variety of machine learning tasks. In this line of work, we have recently introduced an optical machine learning framework, termed as Diffractive Deep Neural Network (D 2 NN) [15], where deep learning and error back-propagation methods are used to design, using a computer, diffractive layers that collectively perform a desired task that the network is trained for.…”

mentioning

confidence: 99%

Analysis of Diffractive Optical Neural Networks and Their Integration With Electronic Neural Networks

Mengü

Luo

Rivenson

et al. 2020

IEEE J. Select. Topics Quantum Electron.

169

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Optical machine learning offers advantages in terms of power efficiency, scalability, and computation speed. Recently, an optical machine learning method based on diffractive deep neural networks (D 2 NNs) has been introduced to execute a function as the input light diffracts through passive layers, designed by deep learning using a computer. Here, we introduce improvements to D 2 NNs by changing the training loss function and reducing the impact of vanishing gradients in the error back-propagation step. Using five phase-only diffractive layers, we numerically achieved a classification accuracy of 97.18% and 89.13% for optical recognition of handwritten digits and fashion products, respectively; using both phase and amplitude modulation (complex-valued) at each layer, our inference performance improved to 97.81% and 89.32%, respectively. Furthermore, we report the integration of D 2 NNs with electronic neural networks to create hybrid classifiers that significantly reduce the number of input pixels into an electronic network using an ultra-compact front-end D 2 NN with a layer-to-layer distance of a few wavelengths, also reducing the complexity of the successive electronic network. Using a five-layer phase-only D 2 NN jointly optimized with a single fully connected electronic layer, we achieved a classification accuracy of 98.71% and 90.04% for the recognition of handwritten digits and fashion products, respectively. Moreover, the input to the electronic network was compressed by >7.8 times down to 10 × 10 pixels. Beyond creating low-power and high-frame rate machine learning platforms, D 2 NN-based hybrid neural networks will find applications in smart optical imager and sensor design. Index Terms-All-optical neural networks, deep learning, hybrid neural networks, optical computing, optical networks, optoelectronic neural networks.

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mentioning

confidence: 99%

Analysis of Diffractive Optical Neural Networks and Their Integration With Electronic Neural Networks

Mengü

Luo

Rivenson

et al. 2020

IEEE J. Select. Topics Quantum Electron.

169

145

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“…Furthermore, deep learning and related optimization tools have been harnessed to find data-driven solutions for various inverse problems arising in, e.g., microscopy [18][19][20][21][22], nanophotonic designs and plasmonics [23][24][25]. These demonstrations and others have been motivating some of the recent advances in optical neural networks and related optical computing techniques that aim to exploit the computational speed, power-efficiency, scalability and parallelization capabilities of optics for machine intelligence applications [26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45].…”

Section: Introductionmentioning

confidence: 99%

Misalignment resilient diffractive optical networks

et al. 2020

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AbstractAs an optical machine learning framework, Diffractive Deep Neural Networks (D2NN) take advantage of data-driven training methods used in deep learning to devise light–matter interaction in 3D for performing a desired statistical inference task. Multi-layer optical object recognition platforms designed with this diffractive framework have been shown to generalize to unseen image data achieving, e.g., >98% blind inference accuracy for hand-written digit classification. The multi-layer structure of diffractive networks offers significant advantages in terms of their diffraction efficiency, inference capability and optical signal contrast. However, the use of multiple diffractive layers also brings practical challenges for the fabrication and alignment of these diffractive systems for accurate optical inference. Here, we introduce and experimentally demonstrate a new training scheme that significantly increases the robustness of diffractive networks against 3D misalignments and fabrication tolerances in the physical implementation of a trained diffractive network. By modeling the undesired layer-to-layer misalignments in 3D as continuous random variables in the optical forward model, diffractive networks are trained to maintain their inference accuracy over a large range of misalignments; we term this diffractive network design as vaccinated D2NN (v-D2NN). We further extend this vaccination strategy to the training of diffractive networks that use differential detectors at the output plane as well as to jointly-trained hybrid (optical-electronic) networks to reveal that all of these diffractive designs improve their resilience to misalignments by taking into account possible 3D fabrication variations and displacements during their training phase.

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“…More in details, interesting experimental and numerical all-optical nonlinear module based on saturable and reverse absorption 15,16 , graphene excitable lasers 17,18 , twosection distributed-feedback (DFB) lasers 19 , quantum dots 20 , disks lasers 21,22 , induced transparency in quantum assembly 15 have recently been reported and showed promising results in terms of efficiency and throughput for different kinds of neural network and applications, ranging from convolutional neural network, spiking neural network and reservoir computing. Although, a more straightforward implementation is currently attained by exploiting electro-optic tuned nonlinear materials 23,24 or absorptive modulator directly connected to a photodiode, as shown in 14,[25][26][27][28] . In this case, the photogenerated current, proportional to the detected optical power at the weighted addition, alters the voltage drop on the active material, thus changing its carrier concentration and consequently the effective modal index of the propagating waveguide mode.…”

Section: Introductionmentioning

confidence: 99%

“…The main aspect to consider when designing and engineering an effective EA modulator is, in fact, the variation of the complex refractive index due to applied bias (i.e. carrier tunability), which is inherent to the selected active material 26 . Silicon (Si) is the conventional material choice usually as fabrication facilities can benefit tremendously from the mature Si process.…”

Section: Introductionmentioning

confidence: 99%

ITO-based electro-absorption modulator for photonic neural activation function

et al. 2019

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Recently integrated optics has become an intriguing platform for implementing machine learning algorithms and in particular neural networks. Integrated photonic circuits can straightforwardly perform vector-matrix multiplications with high efficiency and low power consumption by using weighting mechanism through linear optics. Although, this can not be said for the activation function which requires either nonlinear optics or an electro-optic module with an appropriate dynamic range. Even though all-optical nonlinear optics is potentially faster, its current integration is challenging and is rather inefficient. Here we demonstrate an electro-absorption modulator based on an Indium Tin Oxide layer, whose dynamic range is used as nonlinear activation function of a photonic neuron. The nonlinear activation mechanism is based on a photodiode, which integrates the weighed products, and whose photovoltage drives the elecro-absorption modulator. The synapse and neuron circuit is then constructed to execute a 200-node MNIST classification neural network used for benchmarking the nonlinear activation function and compared with an equivalent electronic module.

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Electrooptic Nonlinear Activation Functions for Vector Matrix Multiplications in Optical Neural Networks

Cited by 14 publications

References 1 publication

Analysis of Diffractive Optical Neural Networks and Their Integration With Electronic Neural Networks

Analysis of Diffractive Optical Neural Networks and Their Integration With Electronic Neural Networks

Misalignment resilient diffractive optical networks

ITO-based electro-absorption modulator for photonic neural activation function

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