ApHMM: Accelerating Profile Hidden Markov Models for Fast and Energy-Efficient Genome Analysis. (arXiv:2207.09765v1 [cs.AR])

Profile hidden Markov models (pHMMs) are widely used in many bioinformatics
applications to accurately identify similarities between biological sequences
(e.g., DNA or protein sequences). PHMMs use a commonly-adopted and
highly-accurate method, called the Baum-Welch algorithm, to calculate these
similarities. However, the Baum-Welch algorithm is computationally expensive,
and existing works provide either software- or hardware-only solutions for a
fixed pHMM design. When we analyze the state-of-the-art works, we find that
there is a pressing need for a flexible, high-performant, and energy-efficient …

analysis ar arxiv energy genome markov profile