Intelligent Multi-channel Meta-imagers for Accelerating Machine Vision

TL;DR

This paper introduces an intelligent meta-imager that uses metasurfaces to perform convolution operations optically, significantly accelerating machine vision tasks with high accuracy, low power, and real-time capability.

Contribution

The work presents a novel optical meta-imager architecture that offloads convolution computations to high-speed, low-power optics, enhancing machine vision efficiency and speed.

Findings

Achieved 98.6% accuracy in handwritten digit classification

Achieved 88.8% accuracy in fashion image classification

Demonstrated high-speed, low-power optical convolution processing

Abstract

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.