Presentation + Paper
8 March 2024 Enhancing and up-scaling of integrated photonic 4F convolution neural networks
Hangbo Yang, Nicola Pesericoa, Shurui Li, Benyamin Fallahi Motlagh, Jaskirat Singh Virdi, Puneet Gupta, Volker J. Sorger, Chee Wei Wong
Author Affiliations +
Abstract
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.
Conference Presentation
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Hangbo Yang, Nicola Pesericoa, Shurui Li, Benyamin Fallahi Motlagh, Jaskirat Singh Virdi, Puneet Gupta, Volker J. Sorger, and Chee Wei Wong "Enhancing and up-scaling of integrated photonic 4F convolution neural networks", Proc. SPIE 12890, Smart Photonic and Optoelectronic Integrated Circuits 2024, 1289003 (8 March 2024); https://doi.org/10.1117/12.2692958
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KEYWORDS
Convolution

Neural networks

Integrated photonics

Modulation

Backscatter

Waveguides

Design

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