An Optoelectronic Reservoir Computing for Temporal Information Processing

作者：Wen Du, Caihong Li, Yixuan Huang, Jihua Zou, Lingzhi Luo, Caihong Teng, Hao‐Chung Kuo, Jiang Wu, Zhiming Wang · 发表于：IEEE Electron Device Letters · 年份：2022 · DOI：10.1109/led.2022.3142257 · 被引用次数：55 · 研究领域：Neural Networks and Reservoir Computing、Advanced Memory and Neural Computing、Neural dynamics and brain function

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 MoS2film. 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.