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 much lower than state-of-the-art electronic neural networks. In this work, we close this gap by introducing 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 nanophotonic neural network reaches 73.80\% 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耗的爆炸式增长，激发了学界对新型计算范式作为传统电子处理器潜在替代方案的浓厚兴趣。光子处理器利用光子而非电子执行运算，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其底层网络设计，在图像识别精度上远低于最先进的电子神经网络。本研究通过引入经低维重参数化技术学习的、大核空间变分卷积神经网络，填补了这一差距。我们利用平面超光学系统实验实现了该网络——该系统包含一组旨在产生角度依赖响应的纳米光子结构阵列。结合一个仅约2000个参数的极轻量级电子后端，我们证明该纳米光子神经网络在CIFAR-10数据集上达到73.80%的盲测分类准确率，这是光学神经网络首次超越首个现代数字神经网络——拥有5700万个参数的AlexNet（72.64%），从而将光学神经网络带入现代深度学习时代。