  The Convolutional Neural Network (CNN) is a state-of-the-art architecture for
a wide range of deep learning problems, the quintessential example of which is
computer vision. CNNs principally employ the convolution operation, which can
be accelerated using the Fourier transform. In this paper, we present an
optical hardware accelerator that combines silicon photonics and free-space
optics, leveraging the use of the optical Fourier transform within several CNN
architectures. The hardware presented is a proof of concept, demonstrating that
this technology can be applied to artificial intelligence problems with a large
efficiency boost with respect to canonical methods.
