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

TL;DR

This paper introduces DAS-MP, a macro placement method that enhances dataflow awareness by considering standard cell clusters, leading to significant improvements in wirelength, congestion, and timing metrics with minimal runtime overhead.

Contribution

DAS-MP is the first approach to incorporate hidden macro-cell connections and dataflow information into macro placement constraints, improving placement quality.

Findings

Achieves 7.9% reduction in wirelength (HPWL)

Reduces congestion overflow by 82.5%

Improves timing metrics with 36.97% WNS and 59.44% TNS improvements

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.