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UID:submissions.supercomputing.org_SC24_sess533_post124@linklings.com
SUMMARY:Scored Non-Deterministic Finite Automata Processor for Sequence Al
 ignment
DESCRIPTION:Ryan Karbowniczak and Rasha Karakchi (University of South Caro
 lina)\n\nWith the surge in symbolic data across various fields, efficient 
 pattern matching and regular expression processing have become crucial. No
 n-deterministic Finite Automata (NFA), commonly used for pattern matching,
  face memory bottlenecks on general-purpose platforms. This has driven int
 erest in Domain-Specific Architectures (DSAs), like FPGA and ASICs, for th
 eir efficiency. Modern applications require identifying the best match pat
 h, such as in DNA sequence alignment. This work aims to enhance FPGA-based
  automata processors to report the best sequence alignment score by integr
 ating weights into automata transitions. Challenges include increased stat
 e-space complexity and memory requirements. The proposed NAPOLY+ design in
 corporates score values and arithmetic components to manage scores, balanc
 ing performance and resource use. Evaluation on the Zynq Ultrascale+ FPGA 
 showed high device utilization and scalability, with preliminary results f
 ocusing on end-to-end design evaluation.\n\nRegistration Category: Tech Pr
 ogram Reg Pass, Exhibits Reg Pass\n\nSession Chairs: Ayesha Afzal (Friedri
 ch-Alexander University, Erlangen-Nuremberg; Erlangen National High Perfor
 mance Computing Center); Sally Ellingson (University of Kentucky); and Ala
 n Sussman (University of Maryland)\n\n
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