As the demand for edge platforms in artificial intelligence increases,including mobile devices and security applications,the surge in data influx into edge devices often triggers interference and suboptimal decision-m...As the demand for edge platforms in artificial intelligence increases,including mobile devices and security applications,the surge in data influx into edge devices often triggers interference and suboptimal decision-making.There is a pressing need for solutions emphasizing low power consumption and cost-effectiveness.In-sensor computing systems employing memristors face challenges in optimizing energy efficiency and streamlining manufacturing due to the necessity for multiple physical processing components.Here,we introduce low-power organic optoelectronic memristors with synergistic optical and mV-level electrical tunable operation for a dynamic“control-on-demand”architecture.Integrating signal sensing,featuring,and processing within the same memristors enables the realization of each in-sensor analogue reservoir computing module,and minimizes circuit integration complexity.The system achieves 97.15%fingerprint recognition accuracy while maintaining a minimal reservoir size and ultra-low energy consumption.Furthermore,we leverage wafer-scale solution techniques and flexible substrates for optimal memristor fabrication.By centralizing core functionalities on the same in-sensor platform,we propose a resilient and adaptable framework for energy-efficient and economical edge computing.展开更多
基金supported by the National Natural Science Foundation of China(62275130,62174089,62375125,and 61761136013)the Natural Science Foundation of Jiangsu Province(BK20240138)+1 种基金the Early Career Scheme(26210623)from the Hong Kong Research Grant Council and Postgraduate Research&Practice Innovation Program of Jiangsu Province(SJCX21_0249).
文摘As the demand for edge platforms in artificial intelligence increases,including mobile devices and security applications,the surge in data influx into edge devices often triggers interference and suboptimal decision-making.There is a pressing need for solutions emphasizing low power consumption and cost-effectiveness.In-sensor computing systems employing memristors face challenges in optimizing energy efficiency and streamlining manufacturing due to the necessity for multiple physical processing components.Here,we introduce low-power organic optoelectronic memristors with synergistic optical and mV-level electrical tunable operation for a dynamic“control-on-demand”architecture.Integrating signal sensing,featuring,and processing within the same memristors enables the realization of each in-sensor analogue reservoir computing module,and minimizes circuit integration complexity.The system achieves 97.15%fingerprint recognition accuracy while maintaining a minimal reservoir size and ultra-low energy consumption.Furthermore,we leverage wafer-scale solution techniques and flexible substrates for optimal memristor fabrication.By centralizing core functionalities on the same in-sensor platform,we propose a resilient and adaptable framework for energy-efficient and economical edge computing.
