Towards On-Chip Optical FFTs for Convolutional Neural Networks

TL;DR

This paper introduces an all-optical on-chip FFT architecture using silicon photonics to significantly accelerate convolutional neural networks, surpassing GPU performance in power efficiency and speed.

Contribution

The paper presents a novel silicon photonics-based architecture for performing FFTs optically, enabling faster and more power-efficient CNN computations compared to traditional electronic methods.

Findings

Achieves up to 10^4 times improvement over GPUs in power-area efficiency.

Demonstrates feasibility of optical FFTs for CNN acceleration.

Provides a detailed design and performance analysis of the optical FFT system.

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 10^4 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.