Alleviation of Temperature Variation Induced Accuracy Degradation in Ferroelectric FinFET Based Neural Network

• URL: http://arxiv.org/abs/2103.03111v1
• Date: Wed, 3 Mar 2021 16:06:03 GMT
• Title: Alleviation of Temperature Variation Induced Accuracy Degradation in Ferroelectric FinFET Based Neural Network
• Authors: Sourav De, Yao-Jen Lee and Darsen D. Lu
• Abstract summary: We adopt a pre-trained artificial neural network with 96.4% inference accuracy on the MNIST dataset as the baseline. We observe a significant inference accuracy degradation in the analog neural network at 233 K for a NN trained at 300 K. We deploy binary neural networks with "read voltage" optimization to ensure immunity of NN to accuracy degradation under temperature variation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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%

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