  The parallelism of optics and the miniaturization of optical components using
nanophotonic structures, such as metasurfaces present a compelling alternative
to electronic implementations of convolutional neural networks. The lack of a
low-power optical nonlinearity, however, requires slow and energy-inefficient
conversions between the electronic and optical domains. Here, we design an
architecture which utilizes a single electrical to optical conversion by
designing a free-space optical frontend unit that implements the linear
operations of the first layer with the subsequent layers realized
electronically. Speed and power analysis of the architecture indicates that the
hybrid photonic-electronic architecture outperforms sole electronic
architecture for large image sizes and kernels. Benchmarking of the
photonic-electronic architecture on a modified version of AlexNet achieves a
classification accuracy of 87% on images from the Kaggle Cats and Dogs
challenge database.
