Rotation Mutation-Driven Evolutionary Algorithm for Efficient Macro Placement

Macro placement is a crucial sub-stage of chip design that focuses on determining the locations of numerous macros while optimizing multiple metrics. In recent years, evolutionary algorithms (EAs) have emerged as a promising technique for enhancing placement performance. However, the solution space of the previous methods is often limited by two-dimensional grids due to insufficient consideration of macro orientation during placement, leading to missed opportunities for further improvement. To address this issue, we propose EA-Rotation for macro placement, which incorporates rotation into the mutation operator to perturb the placement by adjusting the orientation of macros. Utilizing this efficient operator in conjunction with a greedy algorithm, EA-Rotation achieves superior placement solutions. Additionally, EA-Rotation serves as a fine-tuning technique for optimizing both the locations and orientation of macros, significantly improving the placement solutions of existing methods. Comprehensive experiments demonstrate that EA-Rotation outperforms state-of-the-art methods and is effectively integrated with other mutation operators.