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Intelligent Multi-channel Meta-imagers for Accelerating Machine Vision

arXiv 2306.07365 ↗First posted 12 Jun 2023cs.CV physics.opticscited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 57 references that are in the map. First posted on arXiv 12 Jun 2023.

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References · 18

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6 more
  1. 2022Image sensing with multilayer, nonlinear optical neural networkscited by 25
  2. 2018An Optical Frontend for a Convolutional Neural Networkcited by 18
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  6. 2019Intelligent Metasurface Imager and Recognizercited by 5

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