Human emotion recognition with a microcomb-enabled integrated optical neural network Article Swipe
Junwei Cheng
,
Yanzhao Xie
,
Yu Liu
,
Junjie Song
,
Xinyu Liu
,
Zhenming He
,
Wenkai Zhang
,
Xinjie Han
,
Hailong Zhou
,
Ke Zhou
,
Heng Zhou
,
Jianji Dong
,
Xinliang Zhang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1515/nanoph-2023-0298
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1515/nanoph-2023-0298
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.
Related Topics
Concepts
Neuromorphic engineering
Computer science
Artificial neural network
Deep learning
Photonics
Artificial intelligence
Converse
Optical computing
Throughput
Perceptron
Computer architecture
Computer engineering
Electronic engineering
Optics
Physics
Mathematics
Telecommunications
Geometry
Wireless
Engineering
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1515/nanoph-2023-0298
- https://www.degruyter.com/document/doi/10.1515/nanoph-2023-0298/pdf
- OA Status
- gold
- Cited By
- 45
- References
- 58
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387204441
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4387204441Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1515/nanoph-2023-0298Digital Object Identifier
- Title
-
Human emotion recognition with a microcomb-enabled integrated optical neural networkWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-09-29Full publication date if available
- Authors
-
Junwei Cheng, Yanzhao Xie, Yu Liu, Junjie Song, Xinyu Liu, Zhenming He, Wenkai Zhang, Xinjie Han, Hailong Zhou, Ke Zhou, Heng Zhou, Jianji Dong, Xinliang ZhangList of authors in order
- Landing page
-
https://doi.org/10.1515/nanoph-2023-0298Publisher landing page
- PDF URL
-
https://www.degruyter.com/document/doi/10.1515/nanoph-2023-0298/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.degruyter.com/document/doi/10.1515/nanoph-2023-0298/pdfDirect OA link when available
- Concepts
-
Neuromorphic engineering, Computer science, Artificial neural network, Deep learning, Photonics, Artificial intelligence, Converse, Optical computing, Throughput, Perceptron, Computer architecture, Computer engineering, Electronic engineering, Optics, Physics, Mathematics, Telecommunications, Geometry, Wireless, EngineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
45Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 25, 2024: 19, 2023: 1Per-year citation counts (last 5 years)
- References (count)
-
58Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| corresponding_author_ids | https://openalex.org/A5072211609 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 13 |
| corresponding_institution_ids | https://openalex.org/I4210138186, https://openalex.org/I47720641 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.8899999856948853 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.98485973 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |