This paper reports the impacts of temperature variation on the inference accuracy of pre-trained all-ferroelectric FinFET deep neural networks, along with plausible design techniques to abate these impacts. We adopted a pre-trained artificial neural network (NN) with 96.4% inference accuracy on the MNIST dataset as the baseline. As an aftermath of temperature change, the conductance drift of a programmed cell was captured by a compact model over a wide range of gate bias. We observe a significant inference accuracy degradation in the analog neural network at 233 K for a NN trained at 300 K. Finally, we deployed binary neural networks with "read voltage" optimization to ensure immunity of NN to accuracy degradation under temperature variation, maintaining an inference accuracy 96.1%

中文翻译：

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基于铁电FinFET的神经网络中温度变化引起的精度下降的缓解

本文报告了温度变化对预训练的全铁电FinFET深神经网络推理精度的影响，以及减轻这些影响的可行设计技术。我们在MNIST数据集上采用了预训练的人工神经网络（NN），其推理精度为96.4％。作为温度变化的后果，通过紧凑的模型在较大的栅极偏置范围内捕获了已编程单元的电导漂移。对于在300 K训练的NN，我们在233 K的模拟神经网络中观察到明显的推理精度下降。最后，我们部署了具有“读取电压”优化的二进制神经网络，以确保NN在温度变化下不受精度下降的影响，推理准确度96.1％