Cross-layer FeFET Reliability Modeling for Robust Hyperdimensional Computing

Abstract

Hyperdimensional computing (HDC) is an emerging learning paradigm that has gained a lot of attention due to its ability to train with fewer data, lightweight implementation, and resiliency against errors. Similar to the brain, HDC can learn patterns in one iteration from small training data by computing a similarity metric such as Hamming distance. Ferroelectric Field-Effect-Transistor (FeFET) based Ternary Content Addressable Memory (TCAM) has been demonstrated as an excellent candi-date for computing this similarity metric. However, variations in the underlying ferroelectric transistor does impact the reliable HDC operation. In this paper, we demonstrate an end-to-end cross-layer FeFET reliability modeling to obtain robust HDC across the computing stack starting from transistor physics all the way to circuits and systems. The effect of random spatial fluctuation of ferroelectric (FE) domains and other variability sources on electrical characteristics of FeFET is computed through detailed physics-based TCAD simulations. Then, the entire TCAM array is simulated in SPICE using a carefully designed and calibrated compact model to capture the effect of transistor variability on the error probability for individual Hamming distances. Finally, the error probability is employed to compute the loss of inference accuracy of HDC with a language recognition task. We observe very little loss in accuracy even with a high degree of variation.