Accelerating Genome Analysis via Algorithm-Architecture Co-Design

TL;DR

This paper reviews recent advances in accelerating genome analysis through algorithm-architecture co-design, emphasizing integrated architectures to improve performance and energy efficiency amid growing genomic data volumes.

Contribution

It provides a comprehensive overview of recent co-design approaches for genome analysis acceleration and highlights the importance of integrated architectures for performance gains.

Findings

Integrated architectures significantly improve analysis speed

Co-design approaches reduce energy consumption

Emerging technologies enable faster genome processing

Abstract

High-throughput sequencing (HTS) technologies have revolutionized the field of genomics, enabling rapid and cost-effective genome analysis for various applications. However, the increasing volume of genomic data generated by HTS technologies presents significant challenges for computational techniques to effectively analyze genomes. To address these challenges, several algorithm-architecture co-design works have been proposed, targeting different steps of the genome analysis pipeline. These works explore emerging technologies to provide fast, accurate, and low-power genome analysis. This paper provides a brief review of the recent advancements in accelerating genome analysis, covering the opportunities and challenges associated with the acceleration of the key steps of the genome analysis pipeline. Our analysis highlights the importance of integrating multiple steps of genome analysis using suitable architectures to unlock significant performance improvements and reduce data movement and energy consumption. We conclude by emphasizing the need for novel strategies and techniques to address the growing demands of genomic data generation and analysis.