Genome sequence analysis is a powerful tool in medical and scientific research. Considering the inevitable sequencing errors and genetic variations, approximate string matching (ASM) has been adopted in practice for genome sequencing. However, with exponentially increasing bio-data, ASM hardware acceleration is facing severe challenges in improving the throughput and energy efficiency with the accuracy constraint. This paper presents ASMCap, an ASM acceleration approach for genome sequence analysis with hardware-algorithm co-optimization. At the circuit level, ASMCap adopts charge-domain computing based on the capacitive multi-level content addressable memories (ML-CAMs), and outperforms the state-of-the-art ML-CAM-based ASM accelerators EDAM with higher accuracy and energy efficiency. ASMCap also has misjudgment correction capability with two proposed hardware-friendly strategies, namely the Hamming-Distance Aid Correction (HDAC) for the substitution-dominant edits and the Threshold-Aware Sequence Rotation (TASR) for the consecutive indels. Evaluation results show that ASMCap can achieve an average of 1.2x (from 74.7% to 87.6%) and up to 1.8x (from 46.3% to 81.2%) higher F1 score (the key metric of accuracy), 1.4x speedup, and 10.8x energy efficiency improvement compared with EDAM. Compared with the other ASM accelerators, including ResMA based on the comparison matrix, and SaVI based on the seeding strategy, ASMCap achieves an average improvement of 174x and 61x speedup, and 8.7e3x and 943x higher energy efficiency, respectively.

翻译：基因组序列分析是医学和科学研究中的有力工具。考虑到不可避免的测序错误和遗传变异，近似字符串匹配（ASM）已在基因组测序实践中被广泛采用。然而，随着生物数据呈指数级增长，ASM硬件加速在满足精度约束的同时提升吞吐量和能效方面面临严峻挑战。本文提出ASMCap，一种通过硬件-算法协同优化的基因组序列分析ASM加速方法。在电路层面，ASMCap采用基于电容多级内容可寻址存储器（ML-CAM）的电荷域计算，在精度和能效方面优于当前最先进的基于ML-CAM的ASM加速器EDAM。ASMCap还具备误判校正能力，通过两种硬件友好型策略实现：针对替换主导编辑的汉明距离辅助校正（HDAC）和针对连续插入缺失的阈值感知序列旋转（TASR）。评估结果显示，与EDAM相比，ASMCap的F1分数（精度关键指标）平均提升1.2倍（从74.7%提升至87.6%），最高提升1.8倍（从46.3%提升至81.2%），同时实现1.4倍加速和10.8倍能效提升。与基于比较矩阵的ResMA和基于种子策略的SaVI等其他ASM加速器相比，ASMCap分别实现平均174倍和61倍加速，以及8.7e3倍和943倍能效提升。