Fine-grained parallel application specific computing for RNA secondary structure prediction on FPGA

Abstract

In the field of RNA secondary structure prediction, the Zuker algorithm is one of the most popular methods using free energy minimization. However, general-purpose computers including parallel computers or multi-core computers exhibit parallel efficiency of no more than 50% on Zuker. FPGA chips provide a new approach to accelerate the Zuker algorithm by exploiting fine-grained custom design. Zuker shows complicated data dependences, in which the dependence distance is variable, and the dependence direction is also across two dimensions. We propose a systolic array structure including one master PE and multiple slave PEs for fine grain hardware implementation on FPGA. We exploit data reuse schemes to reduce the need to load energy matrices from external memory. We also propose several methods to reduce energy table parameter size by 85%. To our knowledge, our implementation with 16 PEs is the only FPGA accelerator implementing the complete Zuker algorithm. The experimental results show a factor of 14 speedup over the ViennaRNA-1.6.5 software for 2981-residue RNA sequence running on a PC platform with Pentium 4 2.6 GHz CPU.