Optical implementations of neural networks (ONNs) herald the next-generation high-speed and energy-efficient deep learning computing by harnessing the technical advantages of large bandwidth and high parallelism of optics. However, due to the problems of the incomplete numerical domain, limited hardware scale, or inadequate numerical accuracy, the majority of existing ONNs were studied for basic classification tasks. Given that regression is a fundamental form of deep learning and accounts for a large part of current artificial intelligence applications, it is necessary to master deep learning regression for further development and deployment of ONNs. Here, we demonstrate a silicon-based optical coherent dot-product chip (OCDC) capable of completing deep learning regression tasks. The OCDC adopts optical fields to carry out operations in the complete real-value domain instead of in only the positive domain. Via reusing, a single chip conducts matrix multiplications and convolutions in neural networks of any complexity. Also, hardware deviations are compensated via in-situ backpropagation control provided the simplicity of chip architecture. Therefore, the OCDC meets the requirements for sophisticated regression tasks and we successfully demonstrate a representative neural network, the AUTOMAP (a cutting-edge neural network model for image reconstruction). The quality of reconstructed images by the OCDC and a 32-bit digital computer is comparable. To the best of our knowledge, there is no precedent of performing such state-of-the-art regression tasks on ONN chips. It is anticipated that the OCDC can promote the novel accomplishment of ONNs in modern AI applications including autonomous driving, natural language processing, and scientific study.

神经网络 (ONN) 的光学实现通过利用光学的大带宽和高并行性的技术优势预示着下一代高速和节能的深度学习计算。然而，由于数值域不完整、硬件规模有限或数值精度不足等问题，现有的大多数 ONN 都是针对基本分类任务进行研究的。鉴于回归是深度学习的一种基本形式，占当前人工智能应用的很大一部分，因此有必要掌握深度学习回归，以进一步开发和部署 ONN。在这里，我们展示了一种能够完成深度学习回归任务的基于硅的光学相干点积芯片 (OCDC)。OCDC采用光场在完整实值域而不是仅在正域中进行运算。通过重用，单个芯片在任何复杂的神经网络中进行矩阵乘法和卷积。此外，通过原位反向传播控制补偿硬件偏差，提供简单的芯片架构。因此，OCDC 满足复杂回归任务的要求，我们成功地演示了具有代表性的神经网络 AUTOMAP（用于图像重建的尖端神经网络模型）。OCDC 和 32 位数字计算机重建的图像质量相当。据我们所知，在 ONN 芯片上执行这种最先进的回归任务还没有先例。