Abstract: Opto-electronic devices can evaluate convolutional neural networks with reduced power consumption and higher speeds using optical metamaterial structures. Methods and systems for convolution of an input vector f with a kernel k can include a first optical element that is adjustable according to the input vector f and a second optical element that is adjustable according to the kernel k, where either or both elements can include adjustable optical metasurfaces. In some approaches, the second optical element is adjustable according to a Fourier transform of the kernel k and is interposed between first and second lenses or volumetric metamaterials implementing Fourier and inverse Fourier transforms, respectively.

Abstract: Opto-electronic devices can evaluate convolutional neural networks with reduced power consumption and higher speeds using optical metamaterial structures. Methods and systems for convolution of an input vector f with a kernel k can include a first optical element that is adjustable according to the input vector f and a second optical element that is adjustable according to the kernel k, where either or both elements can include adjustable optical metasurfaces. In some approaches, the second optical element is adjustable according to a Fourier transform of the kernel k and is interposed between first and second lenses or volumetric metamaterials implementing Fourier and inverse Fourier transforms, respectively.

Abstract: Opto-electronic devices can evaluate convolutional neural networks with reduced power consumption and higher speeds using optical metamaterial structures. Methods and systems for convolution of an input vector f with a kernel k can include a first optical element that is adjustable according to the input vector f and a second optical element that is adjustable according to the kernel k, where either or both elements can include adjustable optical metasurfaces. In some approaches, the second optical element is adjustable according to a Fourier transform of the kernel k and is interposed between first and second lenses or volumetric metamaterials implementing Fourier and inverse Fourier transforms, respectively.

Abstract: Opto-electronic devices can evaluate convolutional neural networks with reduced power consumption and higher speeds using optical metamaterial structures. Methods and systems for convolution of an input vector f with a kernel k can include a first optical element that is adjustable according to the input vector f and a second optical element that is adjustable according to the kernel k, where either or both elements can include adjustable optical metasurfaces. In some approaches, the second optical element is adjustable according to a Fourier transform of the kernel k and is interposed between first and second lenses or volumetric metamaterials implementing Fourier and inverse Fourier transforms, respectively.