ArticleNanophotonics (Berlin, Germany)2023
Photonic online learning: a perspective.
Article in Nanophotonics (Berlin, Germany), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 citing papers in PubMed.
- Article
- Metrics for spin-based computing.Nature reviews. Physics · 2026Article
- Neuromorphic photonic computing with an electro-optic analog memory.Nature communications · 2026Article
- Online training and pruning of multi-wavelength photonic neural networks.Nanophotonics (Berlin, Germany) · 2025Article
- Emergent Self-Adaptation in an Integrated Photonic Neural Network for Backpropagation-Free Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Neuromorphic computing at scale.Nature · 2025Review
- A self-learning magnetic Hopfield neural network with intrinsic gradient descent adaption.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- 120 GOPS Photonic tensor core in thin-film lithium niobate for inference and in situ training.Nature communications · 2024Article
- Neural network learning with photonics and for photonic circuit design.Nanophotonics (Berlin, Germany) · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Emerging neuromorphic hardware promises to solve certain problems faster and with higher energy efficiency than traditional computing by using physical processes that take place at the device level as the computational primitives in neural networks. While initial results in photonic neuromorphic hardware are very promising, such hardware requires programming or "training" that is often power-hungry and time-consuming. In this article, we examine the online learning paradigm, where the machinery for training is built deeply into the hardware itself. We argue that some form of online learning will be necessary if photonic neuromorphic hardware is to achieve its true potential.
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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.