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Profile hidden Markov models (pHMMs) are widely employed in variousbioinformatics applications to identify similarities between biologicalsequences, such as DNA or protein sequences. In pHMMs, sequences arerepresented as graph structures. These probabilities are subsequently used tocompute the similarity score between a sequence and a pHMM graph. TheBaum-Welch algorithm, a prevalent and highly accurate method, utilizes theseprobabilities to optimize and compute similarity scores. However, theBaum-Welch algorithm is computationally intensive, and existing solutions offereither software-only or hardware-only approaches with fixed pHMM designs. Weidentify an urgent need for a flexible, high-performance, and energy-efficientHW/SW co-design to address the major inefficiencies in the Baum-Welch algorithmfor pHMMs.  We introduce ApHMM, the first flexible acceleration framework designed tosignificantly reduce both computational and energy overheads associated withthe Baum-Welch algorithm for pHMMs. ApHMM tackles the major inefficiencies inthe Baum-Welch algorithm by 1) designing flexible hardware to accommodatevarious pHMM designs, 2) exploiting predictable data dependency patternsthrough on-chip memory with memoization techniques, 3) rapidly filtering outnegligible computations using a hardware-based filter, and 4) minimizingredundant computations.  ApHMM achieves substantial speedups of 15.55x - 260.03x, 1.83x - 5.34x, and27.97x when compared to CPU, GPU, and FPGA implementations of the Baum-Welchalgorithm, respectively. ApHMM outperforms state-of-the-art CPU implementationsin three key bioinformatics applications: 1) error correction, 2) proteinfamily search, and 3) multiple sequence alignment, by 1.29x - 59.94x, 1.03x -1.75x, and 1.03x - 1.95x, respectively, while improving their energy efficiencyby 64.24x - 115.46x, 1.75x, 1.96x.

ApHMM: Accelerating Profile Hidden Markov Models for Fast and Energy-Efficient Genome Analysis