Optical coherent dot-product chip for sophisticated deep learning regression.

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, because of limited hardware scale and incomplete numerical domain, the majority of existing ONNs are merely studied and benchmarked with basic classification tasks. Given that regression is a fundamental form of deep learning and accounts for a large part of current artificial intelligence applications, we demonstrate a silicon-based optical coherent dot-product chip (OCDC) capable of completing deep learning regression tasks. The OCDC adopts optical fields rather than intensities to represent values in the complete real-value domain. It conducts matrix multiplications and convolutions in neural networks of any complexity via reconfiguration and reusing. Hardware deviations are compensated via in-situ backpropagation control owing to the simplicity of chip architecture, thus enhancing the numerical accuracy of analog computing. Therefore, the OCDC meets the fundamental requirement for 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 best of our knowledge, there is no precedent of performing such state-of-the-art regression tasks on ONN chip. It is anticipated that the OCDC can promote novel accomplishment of ONNs in modern AI applications including autonomous driving, natural language processing, medical diagnosis, and scientific study. Moreover, the OCDC and auxiliary electronics have the potential to be monolithically fabricated with CMOS-compatible silicon photonic integration technologies.