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Influence of variability on the performance of HfO2 memristor-based convolutional neural networks
Solid-State Electronics ( IF 1.7 ) Pub Date : 2021-05-28 , DOI: 10.1016/j.sse.2021.108064
R. Romero-Zaliz , E. Pérez , F. Jiménez-Molinos , C. Wenger , J.B. Roldán

A study of convolutional neural networks (CNNs) was performed to analyze the influence of quantization and variability in the network synaptic weights. Different CNNs were considered accounting for the number of convolutional layers, size of the filters in the convolutional layer, number of neurons in the final network layers and different sets of quantization levels. The conductance levels of fabricated 1T1R structures based on HfO2 memristors were considered as reference for four or eight level quantization processes at the inference stage of the CNNs, which were previous trained with the MNIST dataset. We also included the variability of the experimental conductance levels that was found to be Gaussian distributed and was correspondingly modeled for the synaptic weight implementation.



中文翻译:

可变性对基于HfO 2忆阻器的卷积神经网络性能的影响

对卷积神经网络 (CNN) 进行了研究,以分析网络突触权重中量化和可变性的影响。考虑到卷积层的数量、卷积层中滤波器的大小、最终网络层中的神经元数量以及不同的量化级别集,考虑了不同的 CNN。基于 HfO 2忆阻器制造的 1T1R 结构的电导水平被认为是 CNN 推理阶段四或八级量化过程的参考,这些过程之前用 MNIST 数据集进行了训练。我们还包括了实验电导水平的可变性,它被发现是高斯分布的,并相应地为突触权重实现建模。

更新日期:2021-06-05
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