Hyperdimensional computing (HDC) is a brain-inspired computational framework that relies
on long hypervectors (HVs) for learning. In HDC, computational operations consist of simple …

Read map** is a fundamental step in many genomics applications. It is used to identify
potential matches and differences between fragments (called reads) of a sequenced …

A critical step of genome sequence analysis is the map** of sequenced DNA fragments
(ie, reads) collected from an individual to a known linear reference genome sequence (ie …

Pattern searches, a key operation in many data analytic applications, often deal with data
represented by multiple states per dimension. However, hash tables, a common software …

Nearest neighbor (NN) search computations are at the core of many applications such as
few-shot learning, classification, and hyperdimensional computing. As such, efficient …

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 …

In a number of machine learning models, an input query is searched across the trained class
vectors to find the closest feature class vector in cosine similarity metric. However …

Generating the hash values of short subsequences, called seeds, enables quickly
identifying similarities between genomic sequences by matching seeds with a single lookup …

A fast and efficient search function across the database has been a core component for a
number of data-intensive tasks in machine learning, IoT applications, and inference …