To tackle the complexity of state-of-the-art electronic systems, silicon foundries continuously shrink the technology nodes and electronic design automation (EDA) vendors offer hierarchical design flows to decompose systems into smaller blocks. However, such a staged design methodology consists of various levels of abstraction, where margins will be accumulated and result in degradation of the overall design quality. This limits the full use of capabilities of both the technology and EDA tools. In this work, a study of drive granularity of standard cells is performed and an interpolation method is proposed for drive option expansion within original cell libraries. These aim to investigate how industrial synthesis tools deal with the drive strength selection using different granularity sets. In addition, a fully-automated, multi-objective (MO) EDA digital flow is introduced for power, performance, area (PPA) optimisation based on drive strength refinement. This population-based search method better handles the increased difficulty of cell selection when using larger logic libraries, producing better optimised solutions than standard tool flow in this case. The achieved experimental results demonstrate how the improved drive granularity cells overall enhance the quality of designs and how a significant improvement in trading off PPA is achieved by the MOEDA flow.

中文翻译：

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通过改进的驱动粒度标准单元进行多目标数字设计优化

为了解决最先进的电子系统的复杂性，硅代工厂不断缩小技术节点，电子设计自动化（EDA）供应商提供分层设计流程，将系统分解为更小的模块。但是，这种分阶段的设计方法包括各种抽象级别，在这些级别上会累积边距并导致整体设计质量下降。这限制了技术和EDA工具功能的充分利用。在这项工作中，对标准单元的驱动器粒度进行了研究，并提出了一种插值方法来扩展原始单元库中的驱动器选项。这些旨在研究工业综合工具如何使用不同的粒度集处理驱动强度选择。此外，还有一个全自动，引入了多目标（MO）EDA数字流，用于基于驱动强度优化的功率，性能，面积（PPA）优化。当使用较大的逻辑库时，这种基于人群的搜索方法可以更好地处理增加的单元选择难度，在这种情况下，与标准工具流程相比，可以提供更好的优化解决方案。获得的实验结果表明，改进的驱动器粒度单元总体上如何提高设计质量，以及MOEDA流程如何在折衷PPA方面实现显着改善。在这种情况下，与标准工具流程相比，可以提供更好的优化解决方案。获得的实验结果表明，改进的驱动器粒度单元总体上如何提高设计质量，以及MOEDA流程如何在折衷PPA方面实现显着改善。在这种情况下，与标准工具流程相比，可以提供更好的优化解决方案。获得的实验结果表明，改进的驱动器粒度单元总体上如何提高设计质量，以及MOEDA流程如何在折衷PPA方面实现显着改善。