Scalable Softcore Vector Processor for Biosequence Applications

  • Authors:
  • Arpith C. Jacob;Brandon Harris;Jeremy Buhler;Roger Chamberlain;Young H. Cho

  • Affiliations:
  • Washington University in St. Louis;Washington University in St. Louis;Washington University in St. Louis;Washington University in St. Louis;Washington University in St. Louis

  • Venue:
  • FCCM '06 Proceedings of the 14th Annual IEEE Symposium on Field-Programmable Custom Computing Machines
  • Year:
  • 2006

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Abstract

Currently available genome databases are growing exponentially in size1, making it difficult for software analysis tools to keep up. A number of hardware accelerators utilizing special purpose VLSI [1] or reconfigurable hardware [2] have been proposed. However, they are inflexible; support for new applications usually requires a laborious redesign. None of these accelerators can be easily adapted to other applications that require differing hardware resources. The design philosophy of the Softcore Vector Processor is based on two important goals: adaptability and performance. Instruction based execution allows programmable support for a large number of algorithms. The fact that different classes of applications require different subsets of hardware resources, argues for a customizable hardware design built from primitives. The second goal was to achieve programmability without sacrificing performance. The SVP was designed to perform competitively with full custom solutions available in the market.