Modern electronic design automation (EDA) tools can handle the complexity of state-of-the-art electronic systems by decomposing them into smaller blocks or cells, introducing different levels of abstraction and staged design flows. However, throughout each independent-optimised design step, overhead and inefficiency can accumulate in the resulting overall design. Performing design-specific optimisation from a more global viewpoint requires more time due to the larger search space, but has the potential to provide solutions with improved performance. In this work, a fully-automated, multi-objective (MO) EDA flow is introduced to address this issue. It specifically tunes drive strength mapping, preceding physical implementation, through multi-objective population-based search algorithms. Designs are evaluated with respect to their power, performance and area (PPA). The proposed approach is capable of expanding the design space, offering a set of Pareto-optimised trade-off solutions for different case-specific utilisation. We have applied the proposed MOEDA framework to ISCAS-85 benchmark circuits using a commercial 65nm standard cell library. The experimental results demonstrate how the MOEDA flow enhances the solutions initially generated by the standard digital flow, and how simultaneously a significant improvement in PPA metrics is achieved.

中文翻译：

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工业EDA流程中基于单元映射的数字电路多目标优化

现代电子设计自动化（EDA）工具可以将最先进的电子系统分解为较小的块或单元，引入不同级别的抽象和分段设计流程，从而应对复杂的电子系统。但是，在每个独立优化的设计步骤中，开销和效率低下都会累积到最终的总体设计中。从更大的角度来看，执行特定于设计的优化需要更多的时间，这是因为搜索空间更大，但是有潜力提供性能更高的解决方案。在这项工作中，引入了全自动的多目标（MO）EDA流程来解决此问题。它通过基于多目标总体的搜索算法专门调整驱动强度映射，然后进行物理实施。对设计进行评估时要考虑其功能，性能和面积（PPA）。所提出的方法能够扩展设计空间，针对不同的案例使用情况提供一套帕累托优化的权衡解决方案。我们已使用商业65nm标准单元库将拟议的MOEDA框架应用于ISCAS-85基准电路。实验结果表明，MOEDA流程如何增强最初由标准数字流程生成的解决方案，以及如何同时实现PPA指标的显着改善。