Integrated Photonic Neural Networks: Opportunities and Challenges

Liao, Kui; Dai, Tianxiang; Yan, Qiuchen; Hu, Xiaoyong; Gong, Qihuang

doi:10.1021/acsphotonics.2c01516

Cited by 11 publications

(11 citation statements)

References 98 publications

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“…Design of a silicon-graphene all-optical activator Typically, ONNs are composed of at least one hidden layer, which encompasses a linear operation component and a nonlinear operation component. In common ONN schemes 24 , the linear operation is executed by means of an optical interference unit (OIU) comprising a programmable mesh of MZIs for weighting and summation (Fig. 1a).…”

Section: Resultsmentioning

confidence: 99%

Ultrafast Silicon Optical Nonlinear Activator for Neuromorphic Computing

Yan

Zhou

Liu

et al. 2023

Preprint

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Optical neural networks (ONNs) have shown great promise in overcoming the speed and efficiency bottlenecks of artificial neural networks (ANNs). However, the absence of high-speed, energy-efficient nonlinear activators significantly impedes the advancement of ONNs and their extension to ultrafast application scenarios like autonomous vehicles and real-time intelligent signal processing. In this work, we designed and fabricated a novel silicon-based ultrafast all-optical nonlinear activator, leveraging the hybrid integration of silicon slot waveguides, plasmonic slot waveguides, and monolayer graphene. We utilized double-balanced detection and synchronous pump-probe measurement techniques to experimentally evaluate the static and dynamic characteristics of the activators, respectively. Exploiting the exceptional picosecond scale photogenerated carrier relaxation time of graphene, the response time of the activator is markedly reduced to ~93.6 ps. This response time is approximately five times faster than electronic neural networks, establishing our all-optical activator as the fastest known in silicon photonics to our knowledge. Moreover, the all-optical nonlinear activator holds a low threshold power of 5.49 mW and a corresponding power consumption per activation of 0.51 pJ. Furthermore, we confirm its feasibility and capability for use in ONNs by simulation, achieving a high accuracy of 96.8% for MNIST handwritten digit recognition and a mean absolute error of less than 0.1 dB for optical signal-to-noise ratio monitoring of high-speed optical signals. This breakthrough in speed and energy efficiency of all-optical nonlinear activators opens the door to significant improvements in the performance and applicability of ONNs, ushering in a new era of advanced artificial intelligence technologies with enormous potential.

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Section: Resultsmentioning

confidence: 99%

Ultrafast Silicon Optical Nonlinear Activator for Neuromorphic Computing

Yan

Zhou

Liu

et al. 2023

Preprint

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“…Photonic neural networks (PNNs) mark a pioneering approach to neural computing, exploiting the velocity and concurrency of light to enhance information processing efficiency [ 1 , 2 , 3 ]. By capitalizing on optical components and principles, PNNs present compelling remedies to long-standing impediments in traditional electronic neural networks, such as speed constraints and energy consumption [ 2 , 4 ]. PNNs embrace a spectrum of architectures, spanning feedforward, recurrent, convolutional, and spiking neural networks, each meticulously crafted for distinct tasks and domains [ 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

“…PNNs leverage optical technologies to perform certain aspects of neural network computation, offering potential benefits in terms of speed, energy efficacy, and parallelism. Several types of PNNs were proposed and studied [ 2 ], which are discussed in Section 2 . The prospects and challenges of the PNNs are briefly discussed in Section 3 , and the paper ends with a brief discussion and concluding remarks.…”

Section: Introductionmentioning

confidence: 99%

Exploring Types of Photonic Neural Networks for Imaging and Computing—A Review

Khonina,

Kazanskiy,

Skidanov

et al. 2024

Nanomaterials

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Photonic neural networks (PNNs), utilizing light-based technologies, show immense potential in artificial intelligence (AI) and computing. Compared to traditional electronic neural networks, they offer faster processing speeds, lower energy usage, and improved parallelism. Leveraging light’s properties for information processing could revolutionize diverse applications, including complex calculations and advanced machine learning (ML). Furthermore, these networks could address scalability and efficiency challenges in large-scale AI systems, potentially reshaping the future of computing and AI research. In this comprehensive review, we provide current, cutting-edge insights into diverse types of PNNs crafted for both imaging and computing purposes. Additionally, we delve into the intricate challenges they encounter during implementation, while also illuminating the promising perspectives they introduce to the field.

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“…[2][3][4][5][6] Compared with traditional electronic hardware computing, optical computing offers several advantages, including ultrafast computing speed, 7,8 ultralow energy consumption, 9 and significant potential for parallel computing. 10,11 In recent years, with the rapid development of deep learning, 12 optical computing based on deep learning with different implementation schemes has been increasingly applied to various tasks, 13 such as vowel recognition, 9 image classification, 11,[14][15][16][17] mathematical operations, 7 and matrix operations. [18][19][20][21][22][23][24][25] A diffractive deep neural network (D 2 NN) is a series of successive diffractive layers designed in a computer using error backpropagation and stochastic gradient descent methods.…”

Section: Introductionmentioning

confidence: 99%

Advanced all-optical classification using orbital-angular-momentum-encoded diffractive networks

Zhang,

Liao,

Cheng

et al. 2023

Adv. Photon. Nexus

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As a successful case of combining deep learning with photonics, the research on optical machine learning has recently undergone rapid development. Among various optical classification frameworks, diffractive networks have been shown to have unique advantages in all-optical reasoning. As an important property of light, the orbital angular momentum (OAM) of light shows orthogonality and mode-infinity, which can enhance the ability of parallel classification in information processing. However, there have been few all-optical diffractive networks under the OAM mode encoding. Here, we report a strategy of OAM-encoded diffractive deep neural network (OAM-encoded D 2 NN) that encodes the spatial information of objects into the OAM spectrum of the diffracted light to perform all-optical object classification. We demonstrated three different OAM-encoded D 2 NNs to realize (1) single detector OAM-encoded D 2 NN for single task classification, (2) single detector OAM-encoded D 2 NN for multitask classification, and (3) multidetector OAM-encoded D 2 NN for repeatable multitask classification. We provide a feasible way to improve the performance of all-optical object classification and open up promising research directions for D 2 NN by proposing OAMencoded D 2 NN.

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Integrated Photonic Neural Networks: Opportunities and Challenges

Cited by 11 publications

References 98 publications

Ultrafast Silicon Optical Nonlinear Activator for Neuromorphic Computing

Ultrafast Silicon Optical Nonlinear Activator for Neuromorphic Computing

Exploring Types of Photonic Neural Networks for Imaging and Computing—A Review

Advanced all-optical classification using orbital-angular-momentum-encoded diffractive networks

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