Meta-optic accelerators for object classifiers

Hanyu Zheng, Quan Liu, You Zhou, Ivan I. Kravchenko, Yuankai Huo, Jason Valentine

Research output: Contribution to journalArticlepeer-review

49 Scopus citations

Abstract

Rapid advances in deep learning have led to paradigm shifts in a number of fields, from medical image analysis to autonomous systems. These advances, however, have resulted in digital neural networks with large computational requirements, resulting in high energy consumption and limitations in real-time decision-making when computation resources are limited. Here, we demonstrate a meta-optic–based neural network accelerator that can off-load computationally expensive convolution operations into high-speed and low-power optics. In this architecture, metasurfaces enable both spatial multiplexing and additional information channels, such as polarization, in object classification. End-to-end design is used to co-optimize the optical and digital systems, resulting in a robust classifier that achieves 93.1% accurate classification of handwriting digits and 93.8% accuracy in classifying both the digit and its polarization state. This approach could enable compact, high-speed, and low-power image and information processing systems for a wide range of applications in machine vision and artificial intelligence.

Original languageEnglish
Article numbereabo6410
JournalScience Advances
Volume8
Issue number30
DOIs
StatePublished - Jul 2022

Funding

J.V. and H.Z. acknowledge support from DARPA under contract HR001118C0015. Part of the fabrication process was conducted at the Center for Nanophase Materials Sciences, which is a DOE Office of Science User Facility. The remainder of the fabrication process took place in the Vanderbilt Institute of Nanoscale Science and Engineering (VINSE), and we thank the staff for support.

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