  Genomics is the foundation of precision medicine, global food security and
virus surveillance. Exact-match is one of the most essential operations widely
used in almost every step of genomics such as alignment, assembly, annotation,
and compression. Modern genomics adopts Ferragina-Manzini Index (FM-Index)
augmenting space-efficient Burrows-Wheeler transform (BWT) with additional data
structures to permit ultra-fast exact-match operations. However, FM-Index is
notorious for its poor spatial locality and random memory access pattern. Prior
works create GPU-, FPGA-, ASIC- and even process-in-memory (PIM)-based
accelerators to boost FM-Index search throughput. Though they achieve the
state-of-the-art FM-Index search throughput, the same as all prior conventional
accelerators, FM-Index PIMs process only one DNA symbol after each DRAM row
activation, thereby suffering from poor memory bandwidth utilization.
  In this paper, we propose a hardware accelerator, EXMA, to enhance FM-Index
search throughput. We first create a novel EXMA table with a
multi-task-learning (MTL)-based index to process multiple DNA symbols with each
DRAM row activation. We then build an accelerator to search over an EXMA table.
We propose 2-stage scheduling to increase the cache hit rate of our
accelerator. We introduce dynamic page policy to improve the row buffer hit
rate of DRAM main memory. We also present CHAIN compression to reduce the data
structure size of EXMA tables. Compared to state-of-the-art FM-Index PIMs, EXMA
improves search throughput by $4.9\times$, and enhances search throughput per
Watt by $4.8\times$.
