Author(s): Xu, SF (Xu, Shaofu); Wang, J (Wang, Jing); Shu, HW (Shu, Haowen); Zhang, ZK (Zhang, Zhike); Yi, SC (Yi, Sicheng); Bai, BW (Bai, Bowen); Wang, XJ (Wang, Xingjun); Liu, JG (Liu, Jianguo); Zou, WW (Zou, Weiwen)

Source: LIGHT-SCIENCE & APPLICATIONS Volume: 10 Issue: 1 Article Number: 221 DOI: 10.1038/s41377-021-00666-8 Published: NOV 1 2021

Abstract: 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.

Accession Number: WOS:000713607800001

PubMed ID: 34725322

ISSN: 2047-7538

Full Text: https://www.nature.com/articles/s41377-021-00666-8