A Coarse Grain Reconfigurable Architecture for sequence alignment problems in bio-informatics

Abstract

A Coarse Grain Reconfigurable Architecture (CGRA) tailored for accelerating bio-informatics algorithms is proposed. The key innovation is a light weight bio-informatics processor that can be reconfigured to perform different Add Compare and Select operations of the popular sequencing algorithms. A programmable and scalable architectural platform instantiates an array of such processing elements and allows arbitrary partitioning and scheduling schemes and capable of solving complete sequencing algorithms including the sequential phases and deal with arbitrarily large sequences. The key difference of the proposed CGRA based solution compared to FPGA and GPU based solutions is a much better match of the architecture and algorithm for the core computational need as well as the system level architectural need. This claim is quantified for three popular sequencing algorithms: the Needleman-Wunsch, Smith-Waterman and HMMER. For the same degree of parallelism, we provide a 5 X and 15 X speed-up improvements compared to FPGA and GPU respectively. For the same size of silicon, the advantage grows by a factor of another 10 X.