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CASA: An Energy-Efficient and High-Speed CAM-based SMEM Seeding Accelerator for Genome Alignment

Yi Huang, Lingkun Kong, Dibei Chen, Zhiyu Chen, Xiangyu Kong, Jianfeng Zhu, Konstantinos Mamouras, Shaojun Wei, Kaiyuan Yang, Leibo Liu

2023Year
5Citations
1Top-tier citations

Abstract

Genome analysis is a critical tool in medical and bioscience research, clinical diagnostics and treatment, and disease control and prevention. Seed and extension-based alignment is the main approach in the genome analysis pipeline, and BWA-MEM2, a widely acknowledged tool for genome alignment, performs seeding by searching for super maximal exact match (SMEM). The computation of SMEM searching requires high memory bandwidth and energy consumption, which becomes the main performance bottleneck in BWA-MEM2. State-of-the-Art designs like ERT and GenAx have achieved impressive speed-ups of SMEM-based genome alignment. However, they are constrained by frequent DRAM fetches or computationally intensive intersection calculations for all possible k-mers at every read position.

We present a CAM-based SMEM seeding accelerator for genome alignment (CASA), which circumvents the major throughput and

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