ArticleScience advances2025
RF-photonic deep learning processor with Shannon-limited data movement.
Article in Science advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Lithium Niobate Quadratic Integrated Nonlinear Photonics: Enabling Ultra-Wide Bandwidth and Ultrafast Photonic Engines.Nanophotonics (Berlin, Germany) · 2026Article
- Integrated photonic ultrawideband real-time spectrum sensing for 6G wireless networks.Nature communications · 2026Article
- Disaggregated machine learning via in-physics computing at radio frequency.Science advances · 2026Article
- Compact, reconfigurable, and scalable photonic neurons by modulation-and-weighting microring resonators.eLight · 2026Article
- Quantum-limited stochastic optical neural networks operating at a few quanta per activation.Nature communications · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Edholm's law predicts exponential growth in data rate and spectrum bandwidth for communications. Owing to exponentially increasing deep neural network computing demands and the slowing of Moore's law, new computing paradigms are required for future advanced communications like 6G. Optical neural networks (ONNs) are promising accelerators but struggle with scalability and system overhead. Here, we introduce our multiplicative analog frequency transform optical neural network (MAFT-ONN), an artificial intelligence hardware accelerator that experimentally computes fully analog deep learning on raw radio frequency (RF) signals, performing modulation classification that quickly converges to 95% accuracy. MAFT-ONN also exhibits scalability with nearly 4 million fully analog operations for MNIST digit classification. Because of the Shannon capacity-limited analog data movement, MAFT-ONN is also hundreds of times faster than traditional RF receivers.
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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.