In-Memory Acceleration of Hyperdimensional Genome Matching on Unreliable Emerging Technologies

Novel computer architectures like Compute-in-Memory (CiM) merge the memory and processing units, mimicking the human brain. Simultaneously, Hyperdimensional Computing (HDC) is emerging as a brain-inspired machine learning (ML) approach. Both developments hold promise for the realm of AI and computing, especially for genome-matching tasks, where large data movements overwhelm traditional von Neumann architectures. FeFET is one of the up-and-coming emerging technologies that promises to enable ultra-efficient and compact CiM architectures. However, the adoption of FeFETs is hindered by their 10 nm-thick Ferroelectric (FE) layer and process variation. Thus, calculations with FeFETs have errors (noise) that traditional ML genome-matching models cannot tolerate. To overcome these challenges, this work is the first one to i) present a reliable HDC framework (HDGIM) for highly-scaled (down to merely 3nm), multi-bit FeFET technology, ii) introduce temperature-thickness modeled noise from FeFET to the HDC system, and iii) extensively define the memorization capacity of HDC hyperparameters in order to evaluate the performance before deployment theoretically. Our novel HDC learning framework iteratively uses two models: a full-precision 32-bit HDC model, an ideal model for training, and a reduced bit-precision by a novel quantization method for validation and inference. Our results demonstrate that highly-scaled FeFET, realizing 3-bit and even 4-bit, can withstand any modeled noise given high dimensionality during inference. Considering the noise during model adjustment improves the inherent robustness by almost 9% on the 4-bit case.