A Multilayer Neural Network Merging Image Preprocessing and Pattern Recognition by Integrating Diffusion and Drift Memristors
A Multilayer Neural Network Merging Image Preprocessing and Pattern Recognition by Integrating Diffusion and Drift Memristors
复制标题
通过集成扩散和漂移忆阻器融合图像预处理和模式识别的多层神经网络
DOI：
10.1109/tcds.2020.3003377
复制
发表时间：
2021-09-01
影响因子：
5
通讯作者：
Chang, Sheng
中科院分区：
文献类型：
--
作者：
Tang, Zhiri;Zhu, Ruohua;Chang, Sheng
随着新型忆阻器模型和器件研究的不断深入，集成各种忆阻器模型的神经网络成为近年来的研究热点。然而，最先进的作品仍然只使用漂移忆阻器来构建这样的神经网络。此外，其他一些相关的工作只适用于少数个别的应用，包括模式识别和边缘检测。本文提出了一种新的多层神经网络，在该网络中，扩散和漂移忆阻器模型被应用于构建一个融合图像预处理和模式识别的系统。具体来说，整个网络由两个用于图像预处理的扩散忆阻细胞层和一个用于模式识别的漂移忆阻前馈层组成。实验结果表明，由于图像预处理和模式识别的融合，获得了良好的识别精度的噪声MNIST。此外，由于高效的内存计算和简短的尖峰编码方法，高处理速度，高吞吐量，和整个网络的硬件资源很少。
With the development of research on novel memristor model and device, neural networks by integrating various memristor models have become a hot research topic recently. However, state-of-the-art works still build such neural networks using drift memristor only. Furthermore, some other related works are only applied to a few individual applications, including pattern recognition and edge detection. In this article, a novel kind of multilayer neural network is proposed, in which diffusion and drift memristor models are applied to construct a system merging image preprocessing and pattern recognition. Specifically, the entire network consists of two diffusion memristive cellular layers for image preprocessing and one drift memristive feedforward layer for pattern recognition. The experimental results show that good recognition accuracy of noisy MNIST is obtained due to the fusion of image preprocessing and pattern recognition. Moreover, owing to high-efficiency in-memory computing and brief spiking encoding methods, high processing speed, high throughput, and few hardware resources of the entire network are achieved.