Human emotion recognition with a microcomb-enabled integrated optical neural network

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...

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Bibliographic Details
Published in:Nanophotonics (Berlin, Germany) Germany), 2023-10, Vol.12 (20), p.3883-3894
Main Authors: Cheng, Junwei, Xie, Yanzhao, Liu, Yu, Song, Junjie, Liu, Xinyu, He, Zhenming, Zhang, Wenkai, Han, Xinjie, Zhou, Hailong, Zhou, Ke, Zhou, Heng, Dong, Jianji, Zhang, Xinliang
Format: Article
Language:English
Subjects:
Artificial intelligence
Automatic control
Deep learning
Emotion recognition
Emotions
Feedback control
human emotion recognition
integrated optics
Light speed
Machine learning
Neural networks
optical computing
optical frequency comb
optical neural network
Power consumption
Tensors
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Summary: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.
ISSN:2192-8614
2192-8606
2192-8614
DOI:10.1515/nanoph-2023-0298