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Optical implementations of neural networks (ONNs) herald the next-generationhigh-speed and energy-efficient deep learning computing by harnessing thetechnical advantages of large bandwidth and high parallelism of optics.However, due to the problems of incomplete numerical domain, limited hardwarescale, or inadequate numerical accuracy, the majority of existing ONNs werestudied for basic classification tasks. Given that regression is a fundamentalform of deep learning and accounts for a large part of current artificialintelligence applications, it is necessary to master deep learning regressionfor further development and deployment of ONNs. Here, we demonstrate asilicon-based optical coherent dot-product chip (OCDC) capable of completingdeep learning regression tasks. The OCDC adopts optical fields to carry outoperations in complete real-value domain instead of in only positive domain.Via reusing, a single chip conducts matrix multiplications and convolutions inneural networks of any complexity. Also, hardware deviations are compensatedvia in-situ backpropagation control provided the simplicity of chiparchitecture. Therefore, the OCDC meets the requirements for sophisticatedregression tasks and we successfully demonstrate a representative neuralnetwork, the AUTOMAP (a cutting-edge neural network model for imagereconstruction). The quality of reconstructed images by the OCDC and a 32-bitdigital computer is comparable. To the best of our knowledge, there is noprecedent of performing such state-of-the-art regression tasks on ONN chip. Itis anticipated that the OCDC can promote novel accomplishment of ONNs in modernAI applications including autonomous driving, natural language processing, andscientific study.

Optical coherent dot-product chip for sophisticated deep learning regression