ArticleScience advances2022
Meta-optic accelerators for object classifiers.
Article in Science advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Universal convolution from wave dynamics: photonic processing and encryption in synthetic dimension.Nature communications · 2026Article
- Article
- Diffractive Optical Neural Networks: Pathways Toward Intelligent and Scalable Photonic Computing.Nanophotonics (Berlin, Germany) · 2026Review
- Double-phase metasurface operators for all-optical image processing.Light, science & applications · 2026Article
- Compound Meta-Optics for Advanced Optical Engineering.Sensors (Basel, Switzerland) · 2026Review
- Article
- Metaoptics merging computational optics and optical computing toward intelligent visual perception.Science advances · 2026Review
- Vector analog computing via on-demand metasurface dispersive polarization transformation.Science advances · 2025Article
- Parallelizing analog in-sensor visual processing with arrays of gate-tunable silicon photodetectors.Nature communications · 2025Article
- Beating spectral bandwidth limits for large aperture broadband nano-optics.Nature communications · 2025Article
- Spectral convolutional neural network chip for in-sensor edge computing of incoherent natural light.Nature communications · 2025Article
- Spatially varying nanophotonic neural networks.Science advances · 2024Article
- Functionality Expansion of Guided Mode Radiation via On-Chip Metasurfaces.Nano letters · 2024Article
- Multichannel meta-imagers for accelerating machine vision.Nature nanotechnology · 2024Article
- Roadmap for Optical Metasurfaces.ACS photonics · 2024Review
- Photonic advantage of optical encoders.Nanophotonics (Berlin, Germany) · 2024Article
- Single-shot optical neural network.Science advances · 2023Article
- Super-resolution image display using diffractive decoders.Science advances · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.