There is an ever-growing demand for artificial intelligence. Optical processors, which compute with photons instead of electrons, can fundamentally accelerate the development of artificial intelligence by offering substantially improved computing performance. There has been long-term interest in optically constructing the most widely used artificial-intelligence architecture, that is, artificial neural networks, to achieve brain-inspired information processing at the speed of light. However, owing to restrictions in design flexibility and the accumulation of system errors, existing processor architectures are not reconfigurable and have limited model complexity and experimental performance. Here, we propose the reconfigurable diffractive processing unit, an optoelectronic fused computing architecture based on the diffraction of light, which can support different neural networks and achieve a high model complexity with millions of neurons. Along with the developed adaptive training approach to circumvent system errors, we achieved excellent experimental accuracies for high-speed image and video recognition over benchmark datasets and a computing performance superior to that of cutting-edge electronic computing platforms.

对人工智能的需求不断增长。使用光子而不是电子进行计算的光学处理器可以通过提供显着提高的计算性能从根本上加速人工智能的发展。长期以来，人们一直对光学构建最广泛使用的人工智能架构（即人工神经网络）以实现光速下的大脑启发信息处理感兴趣。然而，由于设计灵活性的限制和系统错误的积累，现有的处理器架构是不可重构的，模型复杂性和实验性能有限。在这里，我们提出了可重构衍射处理单元，一种基于光衍射的光电融合计算架构，它可以支持不同的神经网络并实现具有数百万个神经元的高模型复杂性。连同开发的用于规避系统错误的自适应训练方法，我们在基准数据集上实现了高速图像和视频识别的出色实验精度，以及优于尖端电子计算平台的计算性能。