神经形态工程学
计算机科学
MNIST数据库
人工智能
编码(内存)
计算机视觉
尖峰神经网络
人工神经网络
模式识别(心理学)
作者
Fakun Wang,Fangchen Hu,Mingjin Dai,Song Zhu,Fangyuan Sun,Ruihuan Duan,Chongwu Wang,Jiayue Han,Wenjie Deng,Wenduo Chen,Ming Ye,Song Han,Bo Qiang,Yuhao Jin,Yunda Chua,Nan Chi,Shaohua Yu,Donguk Nam,Sang Hoon Chae,Zheng Liu,Qi Jie Wang
标识
DOI:10.1038/s41467-023-37623-5
摘要
Infrared machine vision system for object perception and recognition is becoming increasingly important in the Internet of Things era. However, the current system suffers from bulkiness and inefficiency as compared to the human retina with the intelligent and compact neural architecture. Here, we present a retina-inspired mid-infrared (MIR) optoelectronic device based on a two-dimensional (2D) heterostructure for simultaneous data perception and encoding. A single device can perceive the illumination intensity of a MIR stimulus signal, while encoding the intensity into a spike train based on a rate encoding algorithm for subsequent neuromorphic computing with the assistance of an all-optical excitation mechanism, a stochastic near-infrared (NIR) sampling terminal. The device features wide dynamic working range, high encoding precision, and flexible adaption ability to the MIR intensity. Moreover, an inference accuracy more than 96% to MIR MNIST data set encoded by the device is achieved using a trained spiking neural network (SNN).
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