SMX: Heterogeneous Architecture for Universal Sequence Alignment Acceleration

Sequence alignment is a fundamental building block for critical applications across multiple fields, such as computational biology and information retrieval. The rapid advancement of genome sequencing technologies and breakthrough generative AI tools, like AlphaFold, has driven an exponential increase in sequencedata production, creating a pressing need for fast and efficient sequence alignment tools to analyze ever-growing biological sequence databases. Notwithstanding the numerous accelerators proposed, from general-purpose architectures (CPUs and GPUs) to domainspecific designs (FPGAs and ASICs), the most efficient solutions suffer from over-specialization and fail to adapt to the wide variety of irregular use cases demanded by practical sequence alignment applications. Thus, it remains a challenge to design an architecture that can balance efficiency and flexibility to meet the demands of real-world alignment applications.

This work introduces SMX, a heterogeneous architecture designed for high-performance sequence alignment that supports various configurations for different sequence types (DNA, protein, and ASCII text) and alignment models (including weighted gaps and substitution matrices). SMX integrates an ISA extension (SMX-1D) for irregular and sequential tasks and a specialized coprocessor (SMX-2D) to accelerate regular and parallel tasks, both orchestrated by the general-purpose core to enable seamless integration with state-of-the-art sequence alignment algorithms. Our results demonstrate that SMX's heterogeneous architecture accelerates different sequence alignment use cases by 256-744× compared to stateof-the-art software implementations when aligning real datasets. Compared to specialized hardware accelerators, SMX delivers up to 18.5× more peak performance per area added while providing greater flexibility to accelerate different use cases. Physical design