Edit distance is a computational grand challenge problem to quantify the minimum number of editing operations required to modify one string of characters to the other, finding many applications of natural language processing. In recent years, relevant and increasing interest has also emerged from deoxyribonucleic acid (DNA) applications, like Next Generation Sequencing and DNA storage technologies. Both applications share two crucial features: i) the information is coded into the four bases of DNA and ii) the level of operational noise is still high causing errors in the data, requiring inclusion in the workflow of the computation of algorithms such as the edit distance for finding similarities between sequences. To boost this computation many solutions are available in the literature. Among them, the FPGAs are largely used since the data domain of those applications is strings of 4 characters represented as two-bit values, inconveniently fitting the basic data types of ordinary CPUs and GPUs, with additional benefits of providing a high level of parallelism and low processing latency. This contribution presents a computing- and energy-efficient design implementing the edit distance algorithm combining metaprogramming and High-Level Synthesis. We also assess the performance of our design targeting recent FPGA-based accelerators. Our solution uses nearly 90% of FPGA basic-block hardware resources achieving about 90% of computing efficiency delivering a maximum throughput of 16.8 TCUPS and an energy efficiency of 46 Mpair/Joule, enabling the use of FPGAs as a new class of accelerators for High Performance Computing in DNA applications.

中文翻译：

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使用 HLS 在基于 FPGA 的加速器上进行高吞吐量编辑距离计算


编辑距离是一个计算大挑战问题，用于量化将一串字符修改为另一串字符所需的最小编辑操作数，从而发现自然语言处理的许多应用。近年来，脱氧核糖核酸 （DNA） 应用也引起了人们越来越大的兴趣，例如下一代测序和 DNA 储存技术。这两个应用程序有两个关键特征：i） 信息被编码到 DNA 的四个碱基中，以及 ii） 操作噪声水平仍然很高，导致数据出错，需要包含在算法计算的工作流程中，例如用于查找序列之间相似性的编辑距离。为了促进这种计算，文献中提供了许多解决方案。其中，FPGA被广泛使用，因为这些应用程序的数据域是表示为两位值的 4 个字符的字符串，不方便适应普通 CPU 和 GPU 的基本数据类型，并具有提供高水平并行性和低处理延迟的额外好处。本文提出了一种计算和节能设计，实现了结合元编程和高级综合的编辑距离算法。我们还评估了针对最近基于 FPGA 的加速器的设计的性能。我们的解决方案使用近 90% 的 FPGA 基本块硬件资源，实现约 90% 的计算效率，提供 16.8 TCUPS 的最大吞吐量和 46 Mpair/Joule 的能效，使 FPGA 能够用作 DNA 应用中高性能计算的新型加速器。