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14 May 2019 Integrated photonic FFTs for convolutional neural networks and real-time processing (Conference Presentation)
Jonathan George, Volker J. Sorger
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Proceedings Volume 10996, Real-Time Image Processing and Deep Learning 2019; 1099606 (2019) https://doi.org/10.1117/12.2518764
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States
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-Zehnder 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.
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Jonathan George and Volker J. Sorger "Integrated photonic FFTs for convolutional neural networks and real-time processing (Conference Presentation)", Proc. SPIE 10996, Real-Time Image Processing and Deep Learning 2019, 1099606 (14 May 2019); https://doi.org/10.1117/12.2518764
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KEYWORDS
Convolutional neural networks
Integrated photonics
Convolution
Silicon photonics
Directional couplers
Electrooptic modulators
Fourier transforms
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