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.

翻译：人工智能计算量和能量消耗的爆炸式增长，激发了人们对于新型计算模式作为传统电子处理器潜在替代方案的浓厚兴趣。利用光子而非电子执行运算的光子处理器，有望实现具有超低延迟和功耗的光学神经网络。然而，现有光学神经网络受限于底层网络设计，其图像识别精度远低于最先进的电子神经网络。在本工作中，我们通过将大规模并行光学计算嵌入到平坦相机光学器件中，在图像被传感器记录之前，于捕获阶段执行神经网络计算，从而弥合了这一差距。具体而言，我们利用大卷积核，并提出了一种通过低维重参数化技术学习的大核空间变卷积神经网络。我们通过一个由纳米光子结构阵列构成的平坦超光学系统，实验性地实现了该网络，该系统被设计为能产生角度依赖的响应。结合一个仅有约2000个参数的极轻量级电子后端，我们展示了一个可重构的纳米光子神经网络在CIFAR-10数据集上达到了72.76%的盲测分类精度，这是光学神经网络首次超越第一个现代数字神经网络——拥有5700万个参数的AlexNet（72.64%），将光学神经网络带入了现代深度学习时代。