ArticleLight, science & applications2021
Optical coherent dot-product chip for sophisticated deep learning regression.
Article in Light, science & applications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
23 citing papers in PubMed, 116 citations in OpenAlex.
- How nanophotonics can drive optical computing toward practical applications.Nature nanotechnology · 2026Review
- Integrated Neuromorphic Photonic Computing for AI Acceleration: Emerging Devices, Network Architectures, and Future Paradigms.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- 65 TOPS optoelectronic multi-core computing unlocking multi-feature fusion enhancement.Nature communications · 2026Article
- Photonic edge intelligence chip for multi-modal sensing, inference and learning.Nature communications · 2025Article
- Integrated lithium niobate photonic computing circuit based on efficient and high-speed electro-optic conversion.Nature communications · 2025Article
- Thin-film lithium niobate photonic circuit for ray tracing acceleration.Nature communications · 2025Article
- Photonic Bayesian Neural Networks: Leveraging Programmable Noise for Robust and Uncertainty-Aware Computing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- All-integrated multidimensional optical sensing with a photonic neuromorphic processor.Science advances · 2025Article
- Investigation of the Etching Resistance of Yttrium Oxyfluoride Coating Deposited via Atmospheric Plasma Spraying Against ClMaterials (Basel, Switzerland) · 2025Article
- Ultra-compact multi-task processor based on in-memory optical computing.Light, science & applications · 2025Article
- High-integrated photonic tensor core utilizing high-dimensional lightwave and microwave multidomain multiplexing.Light, science & applications · 2025Article
- 120 GOPS Photonic tensor core in thin-film lithium niobate for inference and in situ training.Nature communications · 2024Article
- Article
- Monolithic back-end-of-line integration of phase change materials into foundry-manufactured silicon photonics.Nature communications · 2024Article
- Analog spatiotemporal feature extraction for cognitive radio-frequency sensing with integrated photonics.Light, science & applications · 2024Article
- Correlated optical convolutional neural network with "quantum speedup".Light, science & applications · 2024Article
- Redundancy-free integrated optical convolver for optical neural networks based on arrayed waveguide grating.Nanophotonics (Berlin, Germany) · 2024Article
- Dynamic Spectral Modulation on Meta-Waveguides Utilizing Liquid Crystal.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Compact optical convolution processing unit based on multimode interference.Nature communications · 2023Article
- All-optical ultrafast ReLU function for energy-efficient nanophotonic deep learning.Nanophotonics (Berlin, Germany) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 3 institutions in 1 country.
Funding
Abstract
Optical implementations of neural networks (ONNs) herald the next-generation high-speed and energy-efficient deep learning computing by harnessing the technical advantages of large bandwidth and high parallelism of optics. However, due to the problems of the incomplete numerical domain, limited hardware scale, or inadequate numerical accuracy, the majority of existing ONNs were studied for basic classification tasks. Given that regression is a fundamental form of deep learning and accounts for a large part of current artificial intelligence applications, it is necessary to master deep learning regression for further development and deployment of ONNs. Here, we demonstrate a silicon-based optical coherent dot-product chip (OCDC) capable of completing deep learning regression tasks. The OCDC adopts optical fields to carry out operations in the complete real-value domain instead of in only the positive domain. Via reusing, a single chip conducts matrix multiplications and convolutions in neural networks of any complexity. Also, hardware deviations are compensated via in-situ backpropagation control provided the simplicity of chip architecture. Therefore, the OCDC meets the requirements for sophisticated regression tasks and we successfully demonstrate a representative neural network, the AUTOMAP (a cutting-edge neural network model for image reconstruction). The quality of reconstructed images by the OCDC and a 32-bit digital computer is comparable. To the best of our knowledge, there is no precedent of performing such state-of-the-art regression tasks on ONN chips. It is anticipated that the OCDC can promote the novel accomplishment of ONNs in modern AI applications including autonomous driving, natural language processing, and scientific study.
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.