ArticleNature communications2024
Low-power scalable multilayer optoelectronic neural networks enabled with incoherent light.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Emerging integrated photonic neural network technologies for artificial intelligence: from devices to systems.National science review · 2026Review
- How nanophotonics can drive optical computing toward practical applications.Nature nanotechnology · 2026Review
- Time-synthetic optical neural networks with stable programmable gain.Nature communications · 2026Article
- Transistor-Level Activation Functions via Two-Gate Designs: From Analog Sigmoid and Gaussian Control to Real-Time Hardware Demonstrations.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Nonlinear optical extreme learner via data reverberation with incoherent light.Science advances · 2026Article
- Positive Feedback Organic-on-Silicon Upconversion Devices.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- In-sensor analog optoelectronic processing of concurrent event and memory signals for dynamic vision sensing.Nature communications · 2025Article
- On-chip quantum states generation by incoherent light.Nature communications · 2025Article
- Multilayer nonlinear diffraction neural networks with programmable and fast ReLU activation function.Nature communications · 2025Article
- Nonlinear inference capacity of fiber-optical extreme learning machines.Nanophotonics (Berlin, Germany) · 2025Article
- Image processing with Optical matrix vector multipliers implemented for encoding and decoding tasks.Light, science & applications · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Optical approaches have made great strides towards the goal of high-speed, energy-efficient computing necessary for modern deep learning and AI applications. Read-in and read-out of data, however, limit the overall performance of existing approaches. This study introduces a multilayer optoelectronic computing framework that alternates between optical and optoelectronic layers to implement matrix-vector multiplications and rectified linear functions, respectively. Our framework is designed for real-time, parallelized operations, leveraging 2D arrays of LEDs and photodetectors connected via independent analog electronics. We experimentally demonstrate this approach using a system with a three-layer network with two hidden layers and operate it to recognize images from the MNIST database with a recognition accuracy of 92% and classify classes from a nonlinear spiral data with 86% accuracy. By implementing multiple layers of a deep neural network simultaneously, our approach significantly reduces the number of read-ins and read-outs required and paves the way for scalable optical accelerators requiring ultra low energy.
Identifiers
What OpenQuestion holds
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.