Elastic EDA: Auto-Scaling Cloud Resources for EDA Tasks via Learning-based Approaches

Utilizing cloud EDA for chip design allows access to on-demand high-performance computing (HPC) resources, significantly reducing development time and costs by eliminating the need for costly on-site infrastructure. Despite its benefits, cloud EDA faces significant challenges, primarily the lack of an effective cost model. A key issue is the absence of a mechanism for designers to accurately gauge the characteristics of their EDA jobs in cloud environment, as the design process involves a multitude of EDA tools and steps, often leading to the over or underestimation of needed computational resources. This problem is exacerbated by the varying computational demands of different designs and constraints. To bridge this knowledge gap, we introduce Elastic EDA, a methodology that harnesses machine learning (ML) to understand the characteristics of a design and its early stages, and to predict the computational needs for subsequent phases throughout the entire EDA flow. This approach effectively aligns design behaviors with computational resources, providing cost-efficient solutions for various cloud EDA scenarios. Compared to previous ML-based predictive frameworks for cloud EDA, the proposed method achieves over 60% higher prediction accuracy and supports various elastic computing environments, maximizing the efficiency of cloud re-sources. Compared to various baseline scheduling configurations in the cloud environment, the proposed framework achieves over 16% mean runtime improvement.