HDGIM: Hyperdimensional Genome Sequence Matching on Unreliable highly scaled FeFET

Abstract

This is the first work to present a reliable application for highly scaled (down to merely 3nm), multi-bit Ferroelectric FET (FeFET) technology. FeFET is one of the up-and-coming emerging technologies that is not only fully compatible with the existing CMOS but does hold the promise to realize ultra-efficient and compact Compute-in-Memory (CiM) architectures. Nevertheless, FeFETs struggle with the 10nm thickness of the Ferroelectric (FE) layer. This makes scaling profoundly challenging if not impossible because thinner FE significantly shrinks the memory window leading to large error probabilities that cannot be tolerated. To overcome these challenges, we propose HDGIM, a hyperdimensional computing framework catered to FeFET in the context of genome sequence matching. Genome Sequence Matching is known to have high computational costs, primarily due to huge data movement that substantially overwhelms von-Neuman architectures. On the one hand, our cross-layer FeFET reliability modeling (starting from device physics to circuits) accurately captures the impact of FE scaling on errors induced by process variation and inherent stochasticity in multi-bit FeFETs. On the other hand, our HDC learning framework iteratively adapts by using two models, a full-precision, ideal model for training and a quantized, noisy version for validation and inference. Our results demonstrate that highly scaled FeFET realizing 3-bit and even 4-bit can withstand any noise given high dimensionality during inference. If we consider the noise during model adjustment, we can improve the inherent robustness compared to adding noise during the matching process.