Intelligent Multi-channel Meta-imagers for Accelerating Machine Vision
Hanyu Zheng Quan Liu Ivan I. Kravchenko Xiaomeng Zhang Yuankai Huo Jason G. Valentine
Rapid developments in machine vision have led to advances in a variety of
industries, from medical image analysis to autonomous systems. These
achievements, however, typically necessitate digital neural networks with heavy
computational requirements, which are limited by high energy consumption and
further hinder real-time decision-making when computation resources are not
accessible. Here, we demonstrate an intelligent meta-imager that is designed to
work in concert with a digital back-end to off-load computationally expensive
convolution operations into high-speed and low-power optics. In this
architecture, metasurfaces enable both angle and polarization multiplexing to
create multiple information channels that perform positive and negatively
valued convolution operations in a single shot. The meta-imager is employed for
object classification, experimentally achieving 98.6% accurate classification
of handwritten digits and 88.8% accuracy in classifying fashion images. With
compactness, high speed, and low power consumption, this approach could find a
wide range of applications in artificial intelligence and machine vision
applications.