  The explosive growth of computation and energy cost of artificial
intelligence has spurred strong interests in new computing modalities as
potential alternatives to conventional electronic processors. Photonic
processors that execute operations using photons instead of electrons, have
promised to enable optical neural networks with ultra-low latency and power
consumption. However, existing optical neural networks, limited by the
underlying network designs, have achieved image recognition accuracy far below
that of state-of-the-art electronic neural networks. In this work, we close
this gap by embedding massively parallelized optical computation into flat
camera optics that perform neural network computation during the capture,
before recording an image on the sensor. Specifically, we harness large kernels
and propose a large-kernel spatially-varying convolutional neural network
learned via low-dimensional reparameterization techniques. We experimentally
instantiate the network with a flat meta-optical system that encompasses an
array of nanophotonic structures designed to induce angle-dependent responses.
Combined with an extremely lightweight electronic backend with approximately 2K
parameters we demonstrate a reconfigurable nanophotonic neural network reaches
72.76\% blind test classification accuracy on CIFAR-10 dataset, and, as such,
the first time, an optical neural network outperforms the first modern digital
neural network -- AlexNet (72.64\%) with 57M parameters, bringing optical
neural network into modern deep learning era.
