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

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 (N.N.) with 96.4% inference accuracy on the MNIST dataset as the baseline. As an aftermath of temperature change, a compact model captured the conductance drift of a programmed cell over a wide range of gate biases. We observed a significant inference accuracy degradation in the analog neural network at 233 K for an N.N. trained at 300 K. Finally, we deployed binary neural networks with "read voltage" optimization to ensure immunity of N.N. to accuracy degradation under temperature variation, maintaining an inference accuracy of 96%. Keywords: Ferroelectric memories

References in corpus (2)

• A computationally efficient compact model for ferroelectric FETs for the simulation of online training of neural networks
• Stochastic Variations in Nanoscale HZO based Ferroelectric finFETs: A Synergistic Approach of READ Optimization and Hybrid Precision Mixed Signal WRITE Operation to Mitigate the Implications on DNN Applications

Cited by in corpus (1)

• Ultra Low Power 3D-Embedded Convolutional Neural Network Cube Based on $\alpha$-IGZO Nanosheet and Bi-Layer Resistive Memory