Nanopore sequencing generates noisy electrical signals that need to be converted into a standard string of DNA nucleotide bases using a computational step called basecalling. The accuracy and speed of basecalling have critical implications for all later steps in genome analysis. Many researchers adopt complex deep learning-based models to perform basecalling without considering the compute demands of such models, which leads to slow, inefficient, and memory-hungry basecallers. Therefore, there is a need to reduce the computation and memory cost of basecalling while maintaining accuracy. Our goal is to develop a comprehensive framework for creating deep learning-based basecallers that provide high efficiency and performance. We introduce RUBICON, a framework to develop hardware-optimized basecallers. RUBICON consists of two novel machine-learning techniques that are specifically designed for basecalling. First, we introduce the first quantization-aware basecalling neural architecture search (QABAS) framework to specialize the basecalling neural network architecture for a given hardware acceleration platform while jointly exploring and finding the best bit-width precision for each neural network layer. Second, we develop SkipClip, the first technique to remove the skip connections present in modern basecallers to greatly reduce resource and storage requirements without any loss in basecalling accuracy. We demonstrate the benefits of RUBICON by developing RUBICALL, the first hardware-optimized basecaller that performs fast and accurate basecalling. Compared to the fastest state-of-the-art basecaller, RUBICALL provides a 3.96x speedup with 2.97% higher accuracy. We show that RUBICON helps researchers develop hardware-optimized basecallers that are superior to expert-designed models.
翻译:纳米孔测序产生嘈杂的电信号,需要借助一种名为数据调用的计算步骤将这些信号转换成标准的DNA核苷酸序列。调用的准确性和速度对后续的所有基因组分析步骤都至关重要。许多研究人员采用复杂的基于深度学习的模型进行调用,而不考虑这种模型的计算需求,这导致调用速度慢、效率低,且需要占用大量内存。因此,有必要在保持准确性的同时降低基因组调用的计算和存储成本。本文旨在开发一种全面的框架,以创建高效且性能卓越的基于深度学习的调用器。我们提出了 RUBICON,这是一个开发硬件优化调用器的框架。RUBICON包括两种专为数据调用而设计的新型机器学习技术。首先,我们引入了第一个专为定制硬件加速平台而设计的量化感知数据调用神经架构搜索(QABAS)框架,与此同时,我们还通过共同探索并找到每个神经网络层的最佳位宽精度,使得调用神经网络架构的能力更强。其次,我们开发了 SkipClip,这是第一个用于去除现代数据调用器中跳跃连接的技术,以极大地减少资源和存储需求,而不影响调用准确率。我们通过开发 RUBICALL 来展示 RUBICON 的优点,这是第一个能够快速、准确进行调用的硬件优化调用器。与速度最快的现有调用器相比,RUBICALL 提供了 3.96 倍的加速和高达 2.97% 的准确率提高。我们展示了 RUBICON 如何帮助研究人员开发优于专家设计模型的硬件优化调用器。