Swordfish：一个使用带有非理想忆阻器的存内计算来评估基于深度神经网络的碱基识别的框架
Swordfish: A Framework for Evaluating Deep Neural Network-based Basecalling using Computation-In-Memory with Non-Ideal Memristors
AI总结：
本文提出软硬件协同设计框架Swordfish，通过纳入真实忆阻器芯片的非理想特性并采用协同设计策略，有效评估和减轻了基于忆阻器的存内计算架构在加速深度神经网络碱基识别时的准确率损失，在真实数据集上实现25.7倍加速且仅损失6.01%准确率。
摘要：
碱基识别是许多基因组分析研究中的关键步骤，它依赖大型深度神经网络（DNN）来实现高准确率。遗憾的是，这些DNN计算缓慢且效率低下，导致序列分析过程出现相当大的延迟和资源限制。使用忆阻器的存内计算（CIM）架构可以显著加速DNN的性能。然而，此类设计固有的器件非理想特性和架构限制会大大降低碱基识别的准确率，而这对准确的基因组分析至关重要。为了促进基于忆阻器的CIM设计在碱基识别中的应用，重要的是（1）对潜在的CIM架构进行全面分析，以及（2）制定有效策略来减轻固有器件非理想特性和架构限制可能带来的不利影响。本文提出了Swordfish，一个新颖的软硬件协同设计框架，可以有效解决上述两个问题。Swordfish包含了来自已表征的真实忆阻器芯片的七种电路和器件限制或非理想特性。Swordfish利用各种软硬件协同设计解决方案，来减轻由于此类非理想特性导致的碱基识别准确率损失。为了证明Swordfish的有效性，我们以Bonito（即最先进、准确且快速的开源碱基识别器）作为案例研究。使用Swordfish的实验结果表明，CIM架构可以现实地加速Bonito在广泛的真实数据集上的运行，平均加速25.7倍，准确率损失为6.01%。
英文摘要：
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%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
