  Optical and hybrid convolutional neural networks (CNNs) recently have become
of increasing interest to achieve low-latency, low-power image classification
and computer vision tasks. However, implementing optical nonlinearity is
challenging, and omitting the nonlinear layers in a standard CNN comes at a
significant reduction in accuracy. In this work, we use knowledge distillation
to compress modified AlexNet to a single linear convolutional layer and an
electronic backend (two fully connected layers). We obtain comparable
performance to a purely electronic CNN with five convolutional layers and three
fully connected layers. We implement the convolution optically via engineering
the point spread function of an inverse-designed meta-optic. Using this hybrid
approach, we estimate a reduction in multiply-accumulate operations from 17M in
a conventional electronic modified AlexNet to only 86K in the hybrid compressed
network enabled by the optical frontend. This constitutes over two orders of
magnitude reduction in latency and power consumption. Furthermore, we
experimentally demonstrate that the classification accuracy of the system
exceeds 93% on the MNIST dataset.
