Basecalling, an essential step in many genome analysis studies, relies on large Deep Neural Networks (DNNs) to achieve high accuracy. Unfortunately, these DNNs are computationally slow and inefficient, leading to considerable delays and resource constraints in the sequence analysis process. A Computation-In-Memory (CIM) architecture using memristors can significantly accelerate the performance of DNNs. However, inherent device non-idealities and architectural limitations of such designs can greatly degrade the basecalling accuracy, which is critical for accurate genome analysis. To facilitate the adoption of memristor-based CIM designs for basecalling, it is important to (1) conduct a comprehensive analysis of potential CIM architectures and (2) develop effective strategies for mitigating the possible adverse effects of inherent device non-idealities and architectural limitations. This paper proposes Swordfish, a novel hardware/software co-design framework that can effectively address the two aforementioned issues. Swordfish incorporates seven circuit and device restrictions or non-idealities from characterized real memristor-based chips. Swordfish leverages various hardware/software co-design solutions to mitigate the basecalling accuracy loss due to such non-idealities. To demonstrate the effectiveness of Swordfish, we take Bonito, the state-of-the-art (i.e., accurate and fast), open-source basecaller as a case study. Our experimental results using Sword-fish show that a CIM architecture can realistically accelerate Bonito for a wide range of real datasets by an average of 25.7x, with an accuracy loss of 6.01%.

翻译：碱基识别是许多基因组分析研究中的关键步骤，其依赖大规模深度神经网络实现高精度。然而，这类深度神经网络计算速度慢且效率低，导致序列分析过程中出现显著延迟与资源瓶颈。基于忆阻器的存内计算（CIM）架构可显著加速深度神经网络的性能，但此类设计中固有的器件非理想性和架构局限性会严重降低碱基识别的精度——这对精准基因组分析至关重要。为促进基于忆阻器的CIM设计方案在碱基识别中的应用，需解决两个重要问题：（1）对潜在CIM架构开展全面分析；（2）制定有效策略以缓解器件非理想性与架构局限性的不利影响。本文提出剑鱼（Swordfish），一种新型软硬件协同设计框架，可有效应对上述两大挑战。该框架整合了来自真实忆阻器芯片的七种电路/器件限制或非理想特性，并利用多种软硬件协同设计方案来缓解因非理想性导致的碱基识别精度损失。为验证其有效性，我们以当前最先进（兼具高精度与高效率）的开源碱基识别器Bonito作为案例研究。基于剑鱼框架的实验结果表明，CIM架构可平均实际加速Bonito处理多领域真实数据集达25.7倍，同时精度损失仅6.01%。