摘要
忆阻器具有纳米级尺寸、低功耗、类似神经突触等优点,在神经计算、图像分类等领域具有广阔的应用前景。本文提出了一种基于忆阻器卷积神经网络的面部表情识别方法,首先基于忆阻器构建了ResNet卷积神经网络,并对ResNet网络进行剪枝操作,然后将ResNet模型的所有卷积层以及全连接层的权重映射为忆阻器十字交叉阵列中忆阻器的忆导值。实验结果显示忆阻器卷积神经网络模型在FER2013数据集上的识别准确率为63.82%,在CK+数据集上的识别准确率为93.95%。相比与原卷积网路,准确率损失仅分别为0.31%和0.76%。最后测试了忆阻器的非理想特性对准确率的影响,为忆阻器神经网络的实际部署提供参考。
Memristors have the advantages of nanoscale size,low power consumption and similar to neural synapses,etc.,and have broad application prospects in neural computing,image classification and other fields.In this paper,a facial expression recognition method based on memristor-based convolutional neural network is proposed.First,a memristor-based ResNet convolutional neural network is constructed and the ResNet network is pruned.Then the weights of all convolutional layers and fully connected layers of the ResNet model are mapped as the memductance values of memristors in the memristive crisscross array.The experimental results show that the recognition accuracy of the memristor-based convolutional neural network model on the FER2013 dataset is 63.82%,and the recognition accuracy on the CK+dataset is 93.95%.Compared with the original convolutional network,the accuracy loss is only 0.31%and 0.76%respectively.Finally,the influence of the non-ideal characteristics of the memristor on the accuracy is tested,which provides a reference for the actual deployment of the memristor-based neural network.
作者
赵益波
蒋文
孟若禹
李业宁
Zhao Yibo;Jiang Wen;Meng Ruoyu;Li Yening(School of Electronic&Information Engineering,Nanjing University of Information Science&Technology,Nanjing 210044,China;Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment,Nanjing 210044,China)
出处
《电子测量技术》
北大核心
2022年第16期93-101,共9页
Electronic Measurement Technology
基金
江苏高校优势学科III期建设工程项目
国家自然科学基金(61871230)
江苏省自然科学基金(BK20181410)项目资助。
关键词
忆阻器神经网络
表情识别
ResNet
卷积神经网络
memristor neural network
facial expression recognition
ResNet
convolutional neural network
