Efficient Implementation of Max-Pooling Algorithm Exploiting History-Effect in Ferroelectric-FinFETs

Convolutional neural networks (CNNs) have become the state-of-the-art tool for image classification, object detection, and segmentation. The max-pooling layer in the CNN architecture leads to a significant enhancement in the classification accuracy by extracting the most prominent features from the feature maps produced by the convolutional layers, reducing the number of computations, and preventing overfitting. However, the conventional digital/analog implementations of the max-pooling layer are energy-hungry. Moreover, compact and energy-efficient hardware implementation of the max-pooling layer is essential for realizing CNNs which may handle complex artificial intelligence (AI)/machine learning (ML) workloads on Internet of Things (IoT) edge devices. To this end, in this work, for the first time, we have proposed a highly scalable, compact, and energy-efficient implementation of the max-pooling algorithm utilizing a single Ferroelectric (Fe)-FinFET. We have designed a novel feature-to-pulse mapping scheme and exploit the history-effect in the polarization state of Fe-FinFETs (which is otherwise undesirable for memory application). Our comprehensive analysis using an experimentally calibrated compact model for the doped-HfO2 ferroelectric capacitor integrated with 14-nm-FinFET technology indicates that the final polarization state of the Fe-FinFET corresponds to the maximum input feature irrespective of the order of application of inputs with a mean relative error of 4.95% while consuming 22.8 fJ of energy. Furthermore, extensive network simulations show that such a small deviation of the max-pooling layer output from the ideal value does not lead to a significant degradation in the classification accuracy (
$< 2$
% drop in network fidelity).