Hardware‐Software Codesign of 2D Neuromorphic Optoelectronic Device for Dynamic Gesture Recognition - Wang, Jiarui; Lin, Yinan; You, Junqi; Yu, Tianze; Meng, Weifan; Sun, Linfeng (Advanced intelligent systems, 2025)
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.