神经形态工程学
油藏计算
光电探测器
计算机科学
数据处理
计算
过程(计算)
人工神经网络
信息处理
GSM演进的增强数据速率
光电导性
边缘计算
人工智能
实时计算
光电子学
材料科学
循环神经网络
数据库
操作系统
生物
神经科学
算法
作者
Wen Du,LI Caihong,Yixuan Huang,Jihua Zou,Lingzhi Luo,Caihong Teng,Hao‐Chung Kuo,Jiang Wu,Zhiming Wang
出处
期刊:IEEE Electron Device Letters
[Institute of Electrical and Electronics Engineers]
日期:2022-03-01
卷期号:43 (3): 406-409
被引量:22
标识
DOI:10.1109/led.2022.3142257
摘要
The implementation of 5G increases the demand for data acquisition, thus increasing the pressure of data processing. Although artificial neural network shows great potential in processing big data, efficient neuromorphic visual system is desired due to the waste of computation resources when processing non-structural visual data. Although reservoir computing (RC) has advantages in temporal information processing, the separation of sensors and RC results in addition cost. Here, an optoelectronic RC system is proposed for temporal information processing in sensors. The reservoir is built on photodetectors based on a non-uniform MoS2 film. The persistent photoconductivity effect of the photodetectors enables mapping different temporal inputs into corresponding reservoir states. The readout layer could be simply trained to identify different reservoir states. As a proof of concept, different classification tasks of numbers are demonstrated. The proposed optoelectronic RC system provides a low training cost strategy for intelligent edge machine visual system to process temporal information.
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