Hardware‐Software Codesign of 2D Neuromorphic Optoelectronic Device for Dynamic Gesture Recognition

In‐sensor reservoir computing has recently gained considerable attention for its efficient training process and advanced integration of sensing, storage, and processing functionalities. However, developing a highly efficient in‐sensor reservoir computing system remains challenging, mainly due to the...

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Vydané v:Advanced intelligent systems Ročník 7; číslo 10
Hlavní autori: Wang, Jiarui, Lin, Yinan, You, Junqi, Yu, Tianze, Meng, Weifan, Sun, Linfeng
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Weinheim John Wiley & Sons, Inc 01.10.2025
Wiley
Predmet:
Co-design
Contact resistance
Data storage
Dynamic characteristics
dynamic gesture recognition
Electrodes
Energy consumption
Gesture recognition
Graphene
Hardware
Memory devices
Neural networks
Neuromorphic computing
Optoelectronic devices
optoelectronic synaptic device
Sensors
Software
Transistors
two‐dimensional memristor
ISSN:2640-4567, 2640-4567
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Shrnutí:In‐sensor reservoir computing has recently gained considerable attention for its efficient training process and advanced integration of sensing, storage, and processing functionalities. However, developing a highly efficient in‐sensor reservoir computing system remains challenging, mainly due to the lack of suitable devices with appropriate architectures. In this study, a graphene/MoSe2‐based ohmic contact optoelectronic synaptic memory device optimized for in‐sensor reservoir computing (RC) is introduced, designed to emulate biological synaptic functions and enable efficient neuromorphic computing. Based on the dynamic characteristics and fading memory of this device, a reservoir computing system for dynamic gesture recognition, including six types of gestures, is stimulated, achieving a recognition rate of 95%. This work provides a potential solution for hardware‐software co‐design in dynamic gesture recognition. Dynamic gesture recognition based on an in‐sensor reservoir and its synaptic behaviors. Dynamic image recognition with in‐sensor function is achieved, leveraging the capabilities of in‐sensor reservoir computing. Such a computing architecture integrates the sensory capabilities of the retina with the computational advantages of the brain, forming an innovative computing framework.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:2640-4567
2640-4567
DOI:10.1002/aisy.202401057