Chen Weilin, Zhang Zhang, Liu Gang
National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Shanghai Jiao Tong University, Shanghai 200240, China.
Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
iScience. 2022 Jan 1;25(1):103729. doi: 10.1016/j.isci.2021.103729. eCollection 2022 Jan 21.
Biological visual system can efficiently handle optical information within the retina and visual cortex of the brain, which suggests an alternative approach for the upgrading of the current low-intelligence, large energy consumption, and complex circuitry of the artificial vision system for high-performance edge computing applications. In recent years, retinomorphic machine vision based on the integration of optoelectronic image sensors and processors has been regarded as a promising candidate to improve this phenomenon. This novel intelligent machine vision technology can perform information preprocessing near or even within the sensor in the front end, thereby reducing the transmission of redundant raw data and improving the efficiency of the back-end processor for high-level computing tasks. In this contribution, we try to present a comprehensive review on the recent progress achieved in this emergent field.
生物视觉系统能够有效地处理视网膜和大脑视觉皮层内的光学信息,这为升级当前用于高性能边缘计算应用的低智能、高能耗且电路复杂的人工视觉系统提供了一种替代方法。近年来,基于光电图像传感器和处理器集成的视网膜形态机器视觉被视为改善这一现象的有前途的候选方案。这种新颖的智能机器视觉技术可以在前端传感器附近甚至在传感器内部执行信息预处理,从而减少冗余原始数据的传输,并提高后端处理器执行高级计算任务的效率。在本论文中,我们试图对这一新兴领域的最新进展进行全面综述。