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首页> 外文会议>IEEE/ACM International Conference On Computer Aided Design >Seed-and-Vote based In-Memory Accelerator for DNA Read Mapping
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Seed-and-Vote based In-Memory Accelerator for DNA Read Mapping

机译：基于种子和投票的内存中加速器，用于DNA读图

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Genome analysis is becoming more important in the fields of forensic science, medicine, and history. Sequencing technologies such as High Throughput Sequencing (HTS) and Third Generation Sequencing (TGS) have greatly accelerated genome sequencing. However, genome read mapping remains significantly slower than sequencing. Because of the enormous amount of data needed, the speed of the data transfer between the memory and the processing unit limits the execution speed. In-memory computing can help address the memory-bandwidth bottleneck by minimizing data transfers. Ternary Content Addressable Memories (TCAMs) have been used in accelerators because of their fast searching capability for seed-and-extend, a popular read mapping approach. Seed-and-vote, another read mapping approach, is faster than the seed-and-extend approach but has lower accuracies when used with very short reads. Since sequencing technology is moving to longer reads, the seed-and-vote approach is becoming more viable. We propose a genome read mapping accelerator that uses approximate TCAM to execute the Fast Seed and Vote algorithm (FSVA) that can map both short and long reads. We achieved 400X acceleration compared to the seed-and-extend approach BWA-MEM on a CPU and 115X acceleration at 30X energy improvement compared to state-of-the-art in-memory accelerator using the seed-and-extend approach at 98.75% accuracy for 100bp reads.
机译：基因组分析在法医学，医学和历史学领域变得越来越重要。高通量测序（HTS）和第三代测序（TGS）等测序技术大大加快了基因组测序的速度。但是，基因组读图仍然比测序慢得多。由于需要大量数据，因此存储器与处理单元之间的数据传输速度限制了执行速度。内存中计算可以通过最大程度地减少数据传输来帮助解决内存带宽瓶颈。三元内容可寻址内存（TCAM）已在加速器中使用，因为它们对种子和扩展（一种流行的读取映射方法）的快速搜索能力。另一种读取映射方法“种子投票”比“种子扩展”方法更快，但与非常短的读取配合使用时准确性较低。由于测序技术正朝着更长的读取方向发展，因此“种子投票法”变得更加可行。我们提出了一种基因组读取映射加速器，该加速器使用近似TCAM来执行可映射短读和长读的快速种子和投票算法（FSVA）。与采用CPU的种子扩展方法BWA-MEM相比，我们获得了400倍的加速，与采用种子扩展方法的最新内存中加速器相比，在98.75％的能耗下，获得了30倍的115X加速100bp读取的准确性。

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