On-Demand Reconfigurable Incoherent Optical Matrix Operator for Real-Time Video Image Display

Meng, Xiangyan; Shi, Nuannuan; Li, Guangyi; Li, Wei; Zhu, Ning; Li, Ming

doi:10.1109/jlt.2022.3227090

Cited by 7 publications

(7 citation statements)

References 45 publications

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“…As seen from Equation ( 14), real-valued VMM can be performed by adjusting the coupling coefficient of each MRR. Additionally, WDM-based CNNs with serial data input approaches have also been reported [35,96,[101][102][103][105][106][107][108][109][110]. As shown in Figure 6d, the weight vector W was still weighted on the amplitudes of the wavelength components, and the 1D data X were serially modulated to the amplitude of the wavelength components.…”

Section: Optical Cnn Based On Wavelength Division Multiplexingmentioning

confidence: 90%

“…Then, wavelength components underwent amplitude modulation to load the weight vector W, which was the multiplication process of elements in two vectors. As a main optical device in the WDM-based CNN, the variable optical amplitude modulators can be implemented using MZIs [95], microring resonators (MRRs) [96][97][98][99][100][101][102][103], phase change materials (PCMs) [39,104], and waveshapers [35,[105][106][107][108][109]. The addition process was conducted using square-law photoelectric detection and the summation of the optical intensity of wavelength components at the photodetector.…”

Section: Optical Cnn Based On Wavelength Division Multiplexingmentioning

confidence: 99%

“…(d) Photonic convolution accelerator [35]. (e) On-demand reconfigurable optical matrix operator [106]. (f) Optical realization of binary convolution based on a dual-drive Mach-Zender (DD-MZM) modulator [110].…”

Section: Content Analysismentioning

confidence: 99%

“…The proposed photonic convolution accelerator cooperates with a fully connected optical layer to classify handwritten digits with an experimental accuracy of 88% on 50 images. With the homologous function (Figure 7e), laser arrays are used to replace the optical frequency comb to improve the robustness of the computing system and obtain an experimental recognition accuracy of 96.01% [106]. The on-demand real-time reconfiguration between the optical perceptron and optical convolution operator is also experimentally demonstrated.…”

Section: Content Analysismentioning

confidence: 99%

See 3 more Smart Citations

Optical Convolutional Neural Networks: Methodology and Advances (Invited)

Meng,

Shi,

et al. 2023

Applied Sciences

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As a leading branch of deep learning, the convolutional neural network (CNN) is inspired by the natural visual perceptron mechanism of living things, showing great application in image recognition, language processing, and other fields. Photonics technology provides a new route for intelligent signal processing with the dramatic potential of its ultralarge bandwidth and ultralow power consumption, which automatically completes the computing process after the signal propagates through the processor with an analog computing architecture. In this paper, we focus on the key enabling technology of optical CNN, including reviewing the recent advances in the research hotspots, overviewing the current challenges and limitations that need to be further overcome, and discussing its potential application.

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Section: Optical Cnn Based On Wavelength Division Multiplexingmentioning

confidence: 90%

Section: Optical Cnn Based On Wavelength Division Multiplexingmentioning

confidence: 99%

Section: Content Analysismentioning

confidence: 99%

Section: Content Analysismentioning

confidence: 99%

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Optical Convolutional Neural Networks: Methodology and Advances (Invited)

Meng,

Shi,

et al. 2023

Applied Sciences

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“…Additionally, the light transmission in the ONN simultaneously implements data processing, which effectively avoids data tidal transmission in the von Neumann computing paradigm. In recent years, ONNs have attracted much interest in the realization of high-speed, large-scale and high-parallel optical neuromorphic hardware, with demonstrations including the use of light diffraction 18 – 24 , light interference 25 – 30 , light scattering 31 , 32 and time-wavelength multiplexing 16 , 33 – 39 . The reported ONNs have been comparable to the state-of-the-art digital processors in terms of efficiency but have revealed a huge leap in computing density 40 , 41 .…”

Section: Introductionmentioning

confidence: 99%

Compact optical convolution processing unit based on multimode interference

Meng

Shi

et al. 2023

Nat Commun

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Convolutional neural networks are an important category of deep learning, currently facing the limitations of electrical frequency and memory access time in massive data processing. Optical computing has been demonstrated to enable significant improvements in terms of processing speeds and energy efficiency. However, most present optical computing schemes are hardly scalable since the number of optical elements typically increases quadratically with the computational matrix size. Here, a compact on-chip optical convolutional processing unit is fabricated on a low-loss silicon nitride platform to demonstrate its capability for large-scale integration. Three 2 × 2 correlated real-valued kernels are made of two multimode interference cells and four phase shifters to perform parallel convolution operations. Although the convolution kernels are interrelated, ten-class classification of handwritten digits from the MNIST database is experimentally demonstrated. The linear scalability of the proposed design with respect to computational size translates into a solid potential for large-scale integration.

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Photonic Tensor Processing Unit With Single Dataflow and Programmable High-Precision Weighting Control

Tang,

Wang,

et al. 2024

J. Lightwave Technol.

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On-Demand Reconfigurable Incoherent Optical Matrix Operator for Real-Time Video Image Display

Cited by 7 publications

References 45 publications

Optical Convolutional Neural Networks: Methodology and Advances (Invited)

Optical Convolutional Neural Networks: Methodology and Advances (Invited)

Compact optical convolution processing unit based on multimode interference

Photonic Tensor Processing Unit With Single Dataflow and Programmable High-Precision Weighting Control

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