ArticleNanophotonics (Berlin, Germany)2023
Human emotion recognition with a microcomb-enabled integrated optical neural network.
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 9 papers.
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Who cites it
9 citing papers in PubMed.
- Ultrafast reconfigurable topological-modulators-based photonic processing accelerator.Nature communications · 2026Article
- Nonlinear optical extreme learner via data reverberation with incoherent light.Science advances · 2026Article
- What is next for LLMs? Pushing the boundaries of next-gen AI computing hardware with photonic chips.Nanophotonics (Berlin, Germany) · 2025Review
- Frequency-multiplexed optical reservoir computing using a microcomb.Nanophotonics (Berlin, Germany) · 2025Article
- Complex-valued matrix-vector multiplication using a scalable coherent photonic processor.Science advances · 2025Article
- Multimodal In-Sensor Computing System Using Integrated Silicon Photonic Convolutional Processor.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Optical neural networks: progress and challenges.Light, science & applications · 2024Review
- Nonlinear photonics on integrated platforms.Nanophotonics (Berlin, Germany) · 2024Review
- Multimodal deep learning using on-chip diffractive optics with in situ training capability.Nature communications · 2024Article
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Authors and funding
13 authors.
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Abstract
State-of-the-art deep learning models can converse and interact with humans by understanding their emotions, but the exponential increase in model parameters has triggered an unprecedented demand for fast and low-power computing. Here, we propose a microcomb-enabled integrated optical neural network (MIONN) to perform the intelligent task of human emotion recognition at the speed of light and with low power consumption. Large-scale tensor data can be independently encoded in dozens of frequency channels generated by the on-chip microcomb and computed in parallel when flowing through the microring weight bank. To validate the proposed MIONN, we fabricated proof-of-concept chips and a prototype photonic-electronic artificial intelligence (AI) computing engine with a potential throughput up to 51.2 TOPS (tera-operations per second). We developed automatic feedback control procedures to ensure the stability and 8 bits weighting precision of the MIONN. The MIONN has successfully recognized six basic human emotions, and achieved 78.5 % accuracy on the blind test set. The proposed MIONN provides a high-speed and energy-efficient neuromorphic computing hardware for deep learning models with emotional interaction capabilities.
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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.