Utilizing Dual-Port FeFETs for Energy-Efficient Binary Neural Network Inference Accelerators

Neuromorphic and in-memory computing architectures using emerging nonvolatile memories (e-NVMs) have emerged as promising solutions for area- and energy-efficient deep neural network (DNN) accele- rators. However, the inherent nonideal behavior of e-NVMs such as limited tuning precision (for multibit synapses), nonlinearity, temporal (cycle-to-cycle), and spatial (device-to-device) variability significantly degrades the performance of DNN accelerators. Recently, binary neural networks (BNNs), with 1-bit weights and activations, have been shown to offer an alternative relaxed approach for training and inference with high accuracy. However, the limited endurance and stuck-at faults of e-NVMs such as resistive (R)RAMs, charge trap memory, and so on limit the efficient implementation of BNN accelerators. Considering the ultrahigh endurance, ultralow switching energy, and CMOS-compatibility of the ferroelectric (Fe)FETs, it becomes imperative to explore their potential for BNN accelerators. To this end, in this work, we present a novel approach for the implementation of BNN inference accelerators utilizing an array of dual-port ferroelectric FETs (FeFETs) as current sinks. The dual-port FeFETs not only decouple the read and write paths (leading to reduced read disturbances) but also exhibit high reliability and voltage compatibility with existing peripheral circuit design since the write voltages are low. Furthermore, we utilize a comparator column approach that requires only half area when compared to other differential weights-based BNN accelerators. Our comprehensive analysis utilizing an experimentally calibrated compact model for dual-port FeFETs indicates that the proposed vector-matrix-multiplication (VMM) implementation exhibits an energy efficiency of 789.89 TOPS/W with a throughput of 0.06 TeraOp/s while achieving an accuracy of 96.39% and 80.8% for image classification task on the MNIST and Fashion MNIST datasets after ex-situ training.