论文标题
Helix：用于加速纳米孔基因组基础呼叫的算法/体系结构共同设计
Helix: Algorithm/Architecture Co-design for Accelerating Nanopore Genome Base-calling
论文作者
论文摘要
纳米孔基因组测序是实现个性化医学，全球粮食安全和病毒监测的关键。最先进的基本呼叫者采用深层神经网络（DNN），将纳米孔测序仪产生的电信号转换为数字DNA符号。一个基于DNN的基本呼叫者消耗的$ 44.5 \％$的纳米孔测序管道总执行时间。但是，很难量化基本呼叫器并构建记忆中的功率处理处理（PIM）来运行量化的基本计数。在本文中，我们提出了一种新型的算法/体系结构共同设计的PIM，以效率高效，准确地加速纳米孔基碱基。从算法的角度来看，我们提出了系统的错误意识训练，以最大程度地减少量化的基本呼叫器中系统错误的数量。从架构的角度来看，我们提出了一个基于低功率SOT-MRAM的ADC阵列来处理模数转换操作并提高先前DNN PIM的功率效率。此外，我们修改了传统的基于NVM的DOT-Prododuct Engine来加速CTC解码操作，并创建SOT-MRAM二进制比较器阵列来处理读取投票。与最先进的PIMS相比，Helix将基本的吞吐量提高了$ 6 \ times $，每瓦的吞吐量$ 11.9 \ times $ $，每$ mm^2 $提高了$ 7.5 \ times $ $ $ \ times $，而不会降低基础接听精度。
Nanopore genome sequencing is the key to enabling personalized medicine, global food security, and virus surveillance. The state-of-the-art base-callers adopt deep neural networks (DNNs) to translate electrical signals generated by nanopore sequencers to digital DNA symbols. A DNN-based base-caller consumes $44.5\%$ of total execution time of a nanopore sequencing pipeline. However, it is difficult to quantize a base-caller and build a power-efficient processing-in-memory (PIM) to run the quantized base-caller. In this paper, we propose a novel algorithm/architecture co-designed PIM, Helix, to power-efficiently and accurately accelerate nanopore base-calling. From algorithm perspective, we present systematic error aware training to minimize the number of systematic errors in a quantized base-caller. From architecture perspective, we propose a low-power SOT-MRAM-based ADC array to process analog-to-digital conversion operations and improve power efficiency of prior DNN PIMs. Moreover, we revised a traditional NVM-based dot-product engine to accelerate CTC decoding operations, and create a SOT-MRAM binary comparator array to process read voting. Compared to state-of-the-art PIMs, Helix improves base-calling throughput by $6\times$, throughput per Watt by $11.9\times$ and per $mm^2$ by $7.5\times$ without degrading base-calling accuracy.
