ArticleNature communications2024
120 GOPS Photonic tensor core in thin-film lithium niobate for inference and in situ training.
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 21 papers.
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The trial behind it
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Who cites it
21 citing papers in PubMed.
- Emerging integrated photonic neural network technologies for artificial intelligence: from devices to systems.National science review · 2026Review
- Programmable photonic neural engine with all-optical nonlinear activation and 40,000 connections.Science advances · 2026Article
- Ultrafast reconfigurable topological-modulators-based photonic processing accelerator.Nature communications · 2026Article
- Spatial-Wavelength Multiplexing Error-Controlled Photonic Analog Computing System.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Inverse-designed nanophotonic neural network accelerators for ultra-compact optical computing.Nature communications · 2026Article
- High-clockrate free-space optical in-memory computing.Light, science & applications · 2026Article
- Neuromorphic photonic computing with an electro-optic analog memory.Nature communications · 2026Article
- Anti-interference diffractive deep neural networks for multi-object recognition.Light, science & applications · 2026Article
- Integrated lithium niobate photonic devices for photonic quantum information science.Nano convergence · 2025Review
- All-optical temporal integration mediated by subwavelength heat antennas.Nature communications · 2025Article
- Fully integrated hybrid multimode-multiwavelength photonic processor with picosecond latency.Nature communications · 2025Article
- Ultra-wideband TFLN modulator with selectively removed slab based on multifunctional BCB platform for high coupling efficiency and suppressed EO relaxation.Nanophotonics (Berlin, Germany) · 2025Article
- Article
- Digital and plasmonic artificial neural networks-Improved nonlinear signal processing at high speed and low complexity.Science advances · 2025Article
- Strong-coupling and high-bandwidth cavity electro-optic modulation for advanced pulse-comb synthesis.Light, science & applications · 2025Article
- Integrated electro-optic digital-to-analog link for efficient computing and arbitrary waveform generation.Nature photonics · 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
- Hypermultiplexed integrated photonics-based optical tensor processor.Science advances · 2025Article
- Resource-efficient photonic networks for next-generation AI computing.Light, science & applications · 2025Article
Corrections and comments
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Authors and funding
30 authors.
Funding
Abstract
Photonics offers a transformative approach to artificial intelligence (AI) and neuromorphic computing by enabling low-latency, high-speed, and energy-efficient computations. However, conventional photonic tensor cores face significant challenges in constructing large-scale photonic neuromorphic networks. Here, we propose a fully integrated photonic tensor core, consisting of only two thin-film lithium niobate (TFLN) modulators, a III-V laser, and a charge-integration photoreceiver. Despite its simple architecture, it is capable of implementing an entire layer of a neural network with a computational speed of 120 GOPS, while also allowing flexible adjustment of the number of inputs (fan-in) and outputs (fan-out). Our tensor core supports rapid in-situ training with a weight update speed of 60 GHz. Furthermore, it successfully classifies (supervised learning) and clusters (unsupervised learning) 112 × 112-pixel images through in-situ training. To enable in-situ training for clustering AI tasks, we offer a solution for performing multiplications between two negative numbers.
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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.