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However, developing a highly efficient in\u2010sensor reservoir computing system remains challenging, mainly due to the lack of suitable devices with appropriate architectures. In this study, a graphene\/MoSe\n                    <jats:sub>2<\/jats:sub>\n                    \u2010based ohmic contact optoelectronic synaptic memory device optimized for in\u2010sensor 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\u2010software co\u2010design in dynamic gesture recognition.\n                  <\/jats:p>","DOI":"10.1002\/aisy.202401057","type":"journal-article","created":{"date-parts":[[2025,2,17]],"date-time":"2025-02-17T21:59:37Z","timestamp":1739829577000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Hardware\u2010Software Codesign of 2D Neuromorphic Optoelectronic Device for Dynamic Gesture Recognition"],"prefix":"10.1002","volume":"7","author":[{"given":"Jiarui","family":"Wang","sequence":"first","affiliation":[{"name":"Centre for Quantum Physics Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE), School of Physics Beijing Institute of Technology  Beijing 100081 China"},{"name":"Beijing Key Lab of Nanophotonics &amp; Ultrafine Optoelectronic Systems School of Physics Beijing Institute of Technology  Beijing 100081 China"}]},{"given":"Yinan","family":"Lin","sequence":"additional","affiliation":[{"name":"Centre for Quantum Physics Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE), School of Physics Beijing Institute of Technology  Beijing 100081 China"},{"name":"Beijing Key Lab of Nanophotonics &amp; 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