DAS-MP: Enabling High-Quality Macro Placement with Enhanced Dataflow Awareness

Xiaotian Zhao, Zixuan Li, Yichen Cai, Tianju Wang, Yushan Pan, Xinfei Guo

Abstract: Dataflow is a critical yet underexplored factor in automatic macro placement, which is becoming increasingly important for developing intelligent design automation techniques that minimize reliance on manual adjustments and reduce design iterations. Existing macro or mixed-size placers with dataflow awareness primarily focus on intrinsic relationships among macros, overlooking the crucial influence of standard cell clusters on macro placement. To address this, we propose DAS-MP, which extracts hidden connections between macros and standard cells and incorporates a series of algorithms to enhance dataflow awareness, integrating them into placement constraints for improved macro placement. To further optimize placement results, we introduce two fine-tuning steps: (1) congestion optimization by taking macro area into consideration, and (2) flipping decisions to determine the optimal macro orientation based on the extracted dataflow information. By integrating enhanced dataflow awareness into placement constraints and applying these fine-tuning steps, the proposed approach achieves an average 7.9% improvement in half-perimeter wirelength (HPWL) across multiple widely used benchmark designs compared to a state-of-the-art dataflow-aware macro placer. Additionally, it significantly improves congestion, reducing overflow by an average of 82.5%, and achieves improvements of 36.97% in Worst Negative Slack (WNS) and 59.44% in Total Negative Slack (TNS). The approach also maintains efficient runtime throughout the entire placement process, incurring less than a 1.5% runtime overhead. These results show that the proposed dataflow-driven methodology, combined with the fine-tuning steps, provides an effective foundation for macro placement and can be seamlessly integrated into existing design flows to enhance placement quality.