神经形态工程学
可重构性
计算机科学
可扩展性
多路复用
记忆电阻器
计算机体系结构
计算机硬件
可重组计算
还原(数学)
嵌入式系统
组分(热力学)
现场可编程门阵列
可视化
仿真
建筑
系统体系结构
控制系统
系统集成
并行处理
时分复用
作者
Chuiying Yang,Jiabin Ye,Yi Zou,Yan Xu,Peng Yang,Shengji Yang,Gengxu Chen,Huipeng Chen
摘要
Reconfigurable volatile/nonvolatile neuromorphic devices integrate brain-like rapid learning with stable memory, providing a critical pathway toward edge-intelligent systems. The integration of reconfigurable devices and light-emitting functionality transforms the display from a passive output terminal into an integral hub of the intelligent system. However, integrating reconfigurable volatile/nonvolatile memory and light-emitting functionality within a single device remains a major challenge because of the inherent conflicts among multiple functions and the constraints of conventional two-terminal control. Here, for the first time, we present a three-terminal reconfigurable volatile/nonvolatile light-emitting memristor. The three-terminal configuration provides control of the emission region and memory states, enabling seamless transition between volatile, nonvolatile, and light-emission operations. By enabling spatial reconfigurability and temporal multiplexing of emission, the device improves scalability and simplifies system architecture and control, achieving a 69.23% reduction in data-transfer volume and a 40% reduction in system component count compared with separated architectures. In addition, it offers direct visual feedback while facilitating continuous learning in neuromorphic computing systems. Finally, we implement an anomaly-detection visualization system based on the reconfigurable volatile/nonvolatile light-emitting memristor, demonstrating its ability to produce direct visual output while processing information, thus enabling an integrated memory-compute-display architecture for next-generation edge-intelligent display systems.
科研通智能强力驱动
Strongly Powered by AbleSci AI