A sensory memory processing system with multi-wavelength synaptic-polychromatic light emission for multi-modal information recognition

Liuting Shan, Qizhen Chen, Rengjian Yu, Changsong Gao, Lujian Liu, Tailiang Guo and Huipeng Chen ()
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Liuting Shan: Fuzhou University
Qizhen Chen: Fuzhou University
Rengjian Yu: Fuzhou University
Changsong Gao: Fuzhou University
Lujian Liu: Fuzhou University
Tailiang Guo: Fuzhou University
Huipeng Chen: Fuzhou University

Nature Communications, 2023, vol. 14, issue 1, 1-11

Abstract: Abstract Realizing multi-modal information recognition tasks which can process external information efficiently and comprehensively is an urgent requirement in the field of artificial intelligence. However, it remains a challenge to achieve simple structure and high-performance multi-modal recognition demonstrations owing to the complex execution module and separation of memory processing based on the traditional complementary metal oxide semiconductor (CMOS) architecture. Here, we propose an efficient sensory memory processing system (SMPS), which can process sensory information and generate synapse-like and multi-wavelength light-emitting output, realizing diversified utilization of light in information processing and multi-modal information recognition. The SMPS exhibits strong robustness in information encoding/transmission and the capability of visible information display through the multi-level color responses, which can implement the multi-level pain warning process of organisms intuitively. Furthermore, different from the conventional multi-modal information processing system that requires independent and complex circuit modules, the proposed SMPS with unique optical multi-information parallel output can realize efficient multi-modal information recognition of dynamic step frequency and spatial positioning simultaneously with the accuracy of 99.5% and 98.2%, respectively. Therefore, the SMPS proposed in this work with simple component, flexible operation, strong robustness, and highly efficiency is promising for future sensory-neuromorphic photonic systems and interactive artificial intelligence.

Date: 2023
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DOI: 10.1038/s41467-023-38396-7

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