RADAR

Abstract

Next Generation Sequencing (NGS) technology has become an indispensable tool for studying genomics, resulting in an exponentially growth of biological data. Booming data volume demands significant computational resources and creates challenges for 'Sequence Alignment', which is the most fundamental application in bioinformatics. Consequently, many researchers exploit both software and hardware methods to accelerate the most widely used sequence alignment algorithm - Basic Local Alignment Search Tool (BLAST). However, prior work suffers from moving huge DNA databases from the storage to computational units. Such data movement is both time and energy consuming. Based on the observation that the bottlenecks of BLAST involve a large amount of comparison operations, we propose a 3D Resistive Random Access Memory (ReRAM) based DNA Alignment Accelerator Architecture (RADAR) which performs most computational operations locally without moving DNA databases. To improve the storage density for various lengths of DNA sequences without damaging the performance, we propose a dense data mapping scheme to handle DNA sequences efficiently and a Tail Bits Duplication (TBD) technique to enable fully parallel computation for RADAR. Experimental results show that RADAR can achieve 5114x speedup and 386x energy reduction when compared to a single CPU. Compared to the Multi-Core/FPGA/GPU based accelerators, RADAR outperforms them between 53x and 1896x in processing speed.