ArticleScience advances2023
Ultrafast dynamic machine vision with spatiotemporal photonic computing.
Article in Science advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Spatiotemporal all-optical diffractive neural networks empowered by spatiotemporal light field manipulation.Light, science & applications · 2026Article
- How nanophotonics can drive optical computing toward practical applications.Nature nanotechnology · 2026Review
- All-optical computing towards 100-GHz clock rates.Light, science & applications · 2026Article
- Music recognition based on diffractive neural networks.iScience · 2026Article
- Diffractive Optical Neural Networks: Pathways Toward Intelligent and Scalable Photonic Computing.Nanophotonics (Berlin, Germany) · 2026Review
- Article
- Experimental demonstration of spatiotemporal analog computation in ultrafast optics.Light, science & applications · 2026Article
- Hundred-layer photonic deep learning.Nature communications · 2025Article
- Acoustic Metasurface for Space-time Reflection Manipulation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Deep learning with photonic neural cellular automata.Light, science & applications · 2024Article
- Article
- Reconfigurable metamaterial processing units that solve arbitrary linear calculus equations.Nature communications · 2024Article
- Photonic neuromorphic architecture for tens-of-task lifelong learning.Light, science & applications · 2024Article
- Redundancy-free integrated optical convolver for optical neural networks based on arrayed waveguide grating.Nanophotonics (Berlin, Germany) · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ultrafast dynamic machine vision in the optical domain can provide unprecedented perspectives for high-performance computing. However, owing to the limited degrees of freedom, existing photonic computing approaches rely on the memory's slow read/write operations to implement dynamic processing. Here, we propose a spatiotemporal photonic computing architecture to match the highly parallel spatial computing with high-speed temporal computing and achieve a three-dimensional spatiotemporal plane. A unified training framework is devised to optimize the physical system and the network model. The photonic processing speed of the benchmark video dataset is increased by 40-fold on a space-multiplexed system with 35-fold fewer parameters. A wavelength-multiplexed system realizes all-optical nonlinear computing of dynamic light field with a frame time of 3.57 nanoseconds. The proposed architecture paves the way for ultrafast advanced machine vision free from the limits of memory wall and will find applications in unmanned systems, autonomous driving, ultrafast science, etc.
Identifiers
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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.