| Autor: |
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 |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Nanophotonics, Vol 12, Iss 20, Pp 3883-3894 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2192-8614 |
| DOI: |
10.1515/nanoph-2023-0298 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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