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Towards on-chip optical FFTs for convolutional neural networks
Author(s): Jonathan K. George; Hani Nejadriahi; Volker J. Sorger
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Paper Abstract

Convolutional neural networks have become an essential element of spatial deep learning systems. In the prevailing architecture, the convolution operation is performed with Fast Fourier Transforms (FFT) electronically in GPUs. The parallelism of GPUs provides an efficiency over CPUs, however both approaches being electronic are bound by the speed and power limits of the interconnect delay inside the circuits. Here we present a silicon photonics based architecture for convolutional neural networks that harnesses the phase property of light to perform FFTs efficiently. Our all-optical FFT is based on nested Mach-Zender Interferometers, directional couplers, and phase shifters, with backend electro-optic modulators for sampling. The FFT delay depends only on the propagation delay of the optical signal through the silicon photonics structures. Designing and analyzing the performance of a convolutional neural network deployed with our on-chip optical FFT, we find dramatic improvements by up to 102 when compared to state-of-the-art GPUs when exploring a compounded figure-of-merit given by power per convolution over area. At a high level, this performance is enabled by mapping the desired mathematical function, an FFT, synergistically onto hardware, in this case optical delay interferometers.

Paper Details

Date Published: 17 September 2018
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Proc. SPIE 10751, Optics and Photonics for Information Processing XII, 107510A (17 September 2018); doi: 10.1117/12.2322067
Show Author Affiliations
Jonathan K. George, The George Washington Univ. (United States)
Hani Nejadriahi, The George Washington Univ. (United States)
Volker J. Sorger, The George Washington Univ. (United States)


Published in SPIE Proceedings Vol. 10751:
Optics and Photonics for Information Processing XII
Abdul A. S. Awwal; Khan M. Iftekharuddin; Mireya García Vázquez, Editor(s)

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