PhotoFourier: accelerating convolutional neural network by employing silicon photonics Fourier transform

Nicola Peserico; Hangbo Yang; Hamed Dalir; Volker Sorger

doi:10.1117/12.3003720

13 March 2024 PhotoFourier: accelerating convolutional neural network by employing silicon photonics Fourier transform

Nicola Peserico, Hangbo Yang, Hamed Dalir, Volker Sorger

Proceedings Volume PC12903, AI and Optical Data Sciences V; PC1290305 (2024) https://doi.org/10.1117/12.3003720
Event: SPIE OPTO, 2024, San Francisco, California, United States

Abstract

Convolution Neural Networks are one of the most used networks for Machine Learning. But convolution operation is one of the most demanding operations for electronic units to perform. Here, we present the first integrated PhotoFourier chip, capable of performing Joint Correlation Transformation on a Silicon Photonic chip, reducing the computation complexity from O(N^2) to O(N) at GHz speed rate.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Nicola Peserico, Hangbo Yang, Hamed Dalir, and Volker Sorger "PhotoFourier: accelerating convolutional neural network by employing silicon photonics Fourier transform", Proc. SPIE PC12903, AI and Optical Data Sciences V, PC1290305 (13 March 2024); https://doi.org/10.1117/12.3003720

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