Analog hardware implemented deep learning models are promising for computation and energy constrained systems such as edge computing devices. However, the analog nature of the device and the associated many noise sources will cause changes to the value of the weights in the trained deep learning models deployed on such devices. In this study, systematic evaluation of the inference performance of trained popular deep learning models for image classification deployed on analog devices has been carried out, where additive white Gaussian noise has been added to the weights of the trained models during inference. It is observed that deeper models and models with more redundancy in design such as VGG are more robust to the noise in general. However, the performance is also affected by the design philosophy of the model, the detailed structure of the model, the exact machine learning task, as well as the datasets.

中文翻译：

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模拟设备上深度学习模型的基准推理性能

由模拟硬件实现的深度学习模型有望用于计算和能源受限的系统，例如边缘计算设备。但是，设备的模拟性质以及相关的许多噪声源将导致部署在此类设备上的经过训练的深度学习模型中权重值的变化。在这项研究中，已对模拟设备上部署的经过训练的流行深度学习模型进行图像分类的推理性能进行了系统评估，其中在推理过程中已将加性高斯白噪声添加到训练模型的权重中。可以看出，更深的模型和设计中具有更多冗余的模型（例如VGG）通常对噪声更鲁棒。但是，性能也会受到模型设计理念的影响，