Broadband Visual Adaption and Image Recognition in a Monolithic Neuromorphic Machine Vision System
Yuchen Cai, Feng Wang, Xinming Wang, Shuhui Li, Yanrong Wang, Jia Yang, Tao Yan, Xueying Zhan, Fengmei Wang, Ruiqing Cheng, Jun He, Zhenxing Wang
Abstract
Abstract Bio‐inspired machine visions have caused wide attentions due to the higher time/power efficiencies over the conventional architectures. Although bio‐mimic photo‐sensors and neuromorphic computing have been individually demonstrated, a complete monolithic vision system has rarely been studied. Here, a neuromorphic machine vision system (NMVS) integrating front‐end retinomorphic sensors and a back‐end convolutional neural network (CNN) based on a single ferroelectric‐semiconductor‐transistor (FST) device structure is reported. As a photo‐sensor, the FST shows a broadband (275–808 nm) retina‐like light adaption function with a large dynamic range of 20.3 stops, and as a unit of the CNN, the FST's weight can be linearly programmed. In total, the NMVS has a high recognition accuracy of 93.0% on a broadband‐dim‐image classification task, which is 20% higher than that of an incomplete system without the retinomorphic sensors. Because of the monolithic unit, the NVMS shows high feasibility for integrated bio‐inspired machine vision systems.