Sun Jinglin, Chen Qilai, Fan Fei, Zhang Zeyulin, Han Tingting, He Zhilong, Wu Zhixin, Yu Zhe, Gao Pingqi, Chen Dazheng, Zhang Bin, Liu Gang
National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Shanghai Jiao Tong University, Shanghai 200240, China.
School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai 200237, China.
Fundam Res. 2022 Aug 5;4(6):1666-1673. doi: 10.1016/j.fmre.2022.06.022. eCollection 2024 Nov.
The hierarchically coordinated processing of visual information with the data degradation characteristic embodies the energy consumption minimization and signal transmission efficiency maximization of brain activities. This inspires machine vision to handle the explosively increased data in real-time. In this contribution, we demonstrate the possibility of constructing a coordinated perceptive computing paradigm with dual-mode organic memristors to emulate the visual processing capability of the brain systems. The 32-state modulation of the device photoresponsivity and conductance photo-induced molecular reconfiguration and electrochemical redox activities enables the execution of computing-in-sensor and computing-in-memory tasks, respectively, which in turn allows the homogeneous hardware integration of a single-layer perceptron and a convolutional neural network for high-efficiency hierarchical visual processing. Compared to the sole optoelectronic CIS mode to recognize visual targets, the dual-mode organic memristor-based coordinated computing scheme demonstrates a 24.5% improvement in the recognition accuracy and 45.8% reduction in the network size.
具有数据退化特性的视觉信息分层协同处理体现了大脑活动的能耗最小化和信号传输效率最大化。这激发了机器视觉实时处理爆炸式增长的数据的能力。在本论文中，我们展示了构建一种具有双模式有机忆阻器的协同感知计算范式以模拟大脑系统视觉处理能力的可能性。该器件光响应性和电导的32状态调制分别通过光诱导分子重排和电化学氧化还原活动，实现了传感器内计算和内存内计算任务的执行，进而允许单层感知器和卷积神经网络进行同质硬件集成，以实现高效的分层视觉处理。与仅使用光电CIS模式识别视觉目标相比，基于双模式有机忆阻器的协同计算方案在识别准确率上提高了24.5%，网络规模缩小了45.8%。
