FFT-based Convolution Neural Network on Silicon Photonics Platform

Peserico, Nicola; T., Schwartz, Russell L.; Yang, Hangbo; Ma, Xiaoxuan; Hosseini, Mostafa; Gupta, Puneet; Dalir, Hamed; Sorger, Volker J.

doi:10.1109/ipc53466.2022.9975735

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…After that, multiple kernel parallel convolution operations based on the optical diffraction orders (Figure 3c) were realized by the same research group [70]. In 2023, using the two-dimensional Dammann grating as the key element for generating multiple displaced images, massive parallelism convolution acceleration was experimentally demonstrated with a computing ac- The on-chip Fourier transform scheme was also investigated to realize the optical CNN [73][74][75][76]. The Fourier transform property of integrated star couplers was simulated to realize the optical CNN in 2020 [74].…”

Section: Optical Cnn Based On Optical Diffractionmentioning

confidence: 99%

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 Optical Diffractionmentioning

confidence: 99%

Optical Convolutional Neural Networks: Methodology and Advances (Invited)

Meng,

Shi,

et al. 2023

Applied Sciences

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show abstract

“…Building on our previous work, [8][9][10][11][12][13][14][15][16][17][18][19][20] we raise a new method to optimize the prototyped programmable on-chip photonic convolution neural network (pCNN), and struct its upgraded potential for achieving higher speeds through its scalability with multiple wavelengths and optical buffers. The programmable on-chip pCNN exhibits high-parallel data processing and swift kernel programmability, offering versatility for various applications without requiring alterations to the physical design of the chip.…”

Section: Introductionmentioning

confidence: 99%

Enhancing and up-scaling of integrated photonic 4F convolution neural networks

Yang,

Pesericoa,

et al. 2024

Smart Photonic and Optoelectronic Integrated Circuits 2024

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Convolution neural networks (CNNs) have garnered significant attention in today's digital world due to their remarkable ability to analyze medical images, recognize speech, translate languages, and make intelligent decisions. However, the convolution operation, which plays a vital role in neural networks, imposes a high computational cost, increasing power consumption and latency, particularly when dealing with large matrices. Integrated photonics presents an exciting opportunity to drastically accelerate CNNs with its virtual O(1) cost and speed-of-light latency, by leveraging the properties of Fourier optics. In this study, we propose the design, fabrication and testing of an integrated photonic system for convolutional neural networks based on the 4F system. Moreover, we introduce an innovative backscattering method to accurately adjust the phase of each micro-disk modulator (MDM), ensuring precise on-chip Fourier transform operations. Furthermore, we demonstrate the system's potential for achieving higher speeds through its scalability with multiple wavelengths and optical buffers.

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FFT-based Convolution Neural Network on Silicon Photonics Platform

Cited by 2 publications

References 6 publications

Optical Convolutional Neural Networks: Methodology and Advances (Invited)

Optical Convolutional Neural Networks: Methodology and Advances (Invited)

Enhancing and up-scaling of integrated photonic 4F convolution neural networks

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