ArticleNature communications2023
Compact optical convolution processing unit based on multimode interference.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
What it found
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Who cites it
38 citing papers in PubMed.
- 192-dimensional wavelength-/mode-/polarization-multiplexed photonic architecture for highly parallel optical computing.National science review · 2026Article
- Integrated Neuromorphic Photonic Computing for AI Acceleration: Emerging Devices, Network Architectures, and Future Paradigms.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- On-chip non-volatile all-optical residual neural network accelerator.Light, science & applications · 2026Article
- Photonic integrated computing engine for concurrent optical computing.Nature communications · 2026Article
- 65 TOPS optoelectronic multi-core computing unlocking multi-feature fusion enhancement.Nature communications · 2026Article
- Universal convolution from wave dynamics: photonic processing and encryption in synthetic dimension.Nature communications · 2026Article
- 2D Materials Powering Neuromorphic Intelligence.Nano-micro letters · 2026Review
- Photonic Mixture-of-Experts for scalable multi-task on-chip optical neural networks.Nature communications · 2026Article
- Article
- Diffractive Optical Neural Networks: Pathways Toward Intelligent and Scalable Photonic Computing.Nanophotonics (Berlin, Germany) · 2026Review
- Microcomb-enabled parallel self- calibration optical convolution streaming processor.Light, science & applications · 2026Article
- Optical logic convolutional neural network.Science advances · 2026Article
- Compact, reconfigurable, and scalable photonic neurons by modulation-and-weighting microring resonators.eLight · 2026Article
- All-optical temporal integration mediated by subwavelength heat antennas.Nature communications · 2025Article
- Photonic edge intelligence chip for multi-modal sensing, inference and learning.Nature communications · 2025Article
- Nonreciprocal surface plasmonic neural network for decoupled bidirectional analogue computing.Nature communications · 2025Article
- Highly efficient photonic convolver via lossless mode-division fan-in.Nature communications · 2025Article
- Multi-wavelength diffractive optical neural network integrated with 2D photonic crystals for joint optical classification.Nanophotonics (Berlin, Germany) · 2025Article
- All-optical convolutional neural network based on phase change materials in silicon photonics platform.Scientific reports · 2025Article
- Thin-film lithium niobate photonic circuit for ray tracing acceleration.Nature communications · 2025Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Convolutional neural networks are an important category of deep learning, currently facing the limitations of electrical frequency and memory access time in massive data processing. Optical computing has been demonstrated to enable significant improvements in terms of processing speeds and energy efficiency. However, most present optical computing schemes are hardly scalable since the number of optical elements typically increases quadratically with the computational matrix size. Here, a compact on-chip optical convolutional processing unit is fabricated on a low-loss silicon nitride platform to demonstrate its capability for large-scale integration. Three 2 × 2 correlated real-valued kernels are made of two multimode interference cells and four phase shifters to perform parallel convolution operations. Although the convolution kernels are interrelated, ten-class classification of handwritten digits from the MNIST database is experimentally demonstrated. The linear scalability of the proposed design with respect to computational size translates into a solid potential for large-scale integration.
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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.