ArticleNanophotonics (Berlin, Germany)2023
All-optical ultrafast ReLU function for energy-efficient nanophotonic deep learning.
Article in Nanophotonics (Berlin, Germany), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed.
- High-speed reservoir computing using photonic integrated circuit optical parametric oscillators.Science advances · 2026Article
- Spatiotemporal all-optical diffractive neural networks empowered by spatiotemporal light field manipulation.Light, science & applications · 2026Article
- Quantum nonlinearity for optical neural computing.Nature communications · 2026Article
- All-optical computing towards 100-GHz clock rates.Light, science & applications · 2026Article
- Femtojoule optical Kerr switching with milliwatt-peak-power in silicon-organic hybrid nanocavity.Nature communications · 2026Article
- Lithium Niobate Quadratic Integrated Nonlinear Photonics: Enabling Ultra-Wide Bandwidth and Ultrafast Photonic Engines.Nanophotonics (Berlin, Germany) · 2026Article
- Femto-joule threshold reconfigurable all-optical nonlinear activators for picosecond pulsed optical neural networks.Light, science & applications · 2026Article
- All-optical doubly resonant cavities for energy-efficient ReLU function in nanophotonic deep learning.PloS one · 2026Article
- Article
- Low-power scalable multilayer optoelectronic neural networks enabled with incoherent light.Nature communications · 2024Article
- Deep learning with photonic neural cellular automata.Light, science & applications · 2024Article
- Ultrashort pulse biphoton source in lithium niobate nanophotonics at 2 μm.Nanophotonics (Berlin, Germany) · 2024Article
- Hardware implementation of memristor-based artificial neural networks.Nature communications · 2024Review
- Nonlinear optical encoding enabled by recurrent linear scattering.Nature photonics · 2024Article
- Inverse design of optical lenses enabled by generative flow-based invertible neural networks.Scientific reports · 2023Article
- Single-shot optical neural network.Science advances · 2023Article
- Photonic elementary cellular automata for simulation of complex phenomena.Light, science & applications · 2023Article
- Neural network learning with photonics and for photonic circuit design.Nanophotonics (Berlin, Germany) · 2023Article
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Authors and funding
7 authors.
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Abstract
In recent years, the computational demands of deep learning applications have necessitated the introduction of energy-efficient hardware accelerators. Optical neural networks are a promising option; however, thus far they have been largely limited by the lack of energy-efficient nonlinear optical functions. Here, we experimentally demonstrate an all-optical Rectified Linear Unit (ReLU), which is the most widely used nonlinear activation function for deep learning, using a periodically-poled thin-film lithium niobate nanophotonic waveguide and achieve ultra-low energies in the regime of femtojoules per activation with near-instantaneous operation. Our results provide a clear and practical path towards truly all-optical, energy-efficient nanophotonic deep learning.
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