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

In the field of RNA secondary structure prediction, the CYK (Coche-Younger-Kasami) algorithm is a most popular methods using SCFG (stochastic context-free grammars) model. However, general purpose parallel computers including SMP multiprocessors or cluster systems exhibit low parallel efficiency and they are too expensive to be used easily for many research institutes. FPGA chips provide a new approach to accelerate the CYK algorithm by exploiting fine-grained custom design. The CYK algorithm shows complicated data dependence, 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 partition tasks by columns and assign tasks to PEs for load balance. We exploit data reuse schemes to reduce the need to load matrix from external memory. To our knowledge, our implementation with 16 PEs is the only FPGA accelerator implementing the complete CYK/inside algorithm. The experimental results show a factor of more than 14 speedup over the Infernal-0.55 software running on a PC platform with Pentium 4 2.66GHz CPU. The computational power of our platform with FPGA accelerator is comparable to a PC cluster consisting of 20 Intel-Xeon CPUs for RNA secondary structure prediction using SCFGs, but the hardware cost and power consumption is only about 15% and 10% of the latter respectively.