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 3.19x 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:Nanopore 测序产生噪音的电信号,需要用称为基调调用的计算步骤,将这种信号转换成一个标准的DNA核糖核酸基地。基调的准确性和速度对基因组分析中后来的所有步骤都具有重要影响。许多研究人员采用复杂的深深层次学习模型进行基调,而不考虑这类模型的计算要求,从而导致缓慢、低效和记忆-饥饿的基调呼叫器。因此,有必要降低基调呼叫的计算和记忆成本,同时保持准确性。我们的目标是开发一个综合框架,用于创建深层次的基于学习的基调呼叫器,提供高效和性能。我们引入了RUBCON,这是开发硬性计算模型的两种新的机器学习技术。首先,我们引入了首个基调调调的计算器计算器计算和记忆-内存成本的网络结构(QABAS)框架,目的是在共同探索和寻找最佳的更高层次基调基调基调基调的精度时,我们开发了最精确性基调的基调直径直径直径直径直径直,我们开发了基调基调的基调的基调的基调直径直径直径直压模型。