MAD-EA: A Multi-Mask Driven Evolutionary Algorithm Framework for Macro Placement Refinement

Macro placement (MP) is essential in designing very large-scale integrated (VLSI) circuits. However, existing MP approaches face two main challenges: the adaptability to different objectives and the consideration of potential impacts on subsequent workflows. To overcome these challenges, an intuitive approach is to refine the placements generated by existing placers, known as macro placement refinement. This paper introduces a novel multi-mask driven evolutionary algorithm framework, MAD-EA, for intelligent adaptive MP refinement. MAD-EA can be seamlessly integrated as a plugin with any placer to refine initial placements. It incorporates diverse metrics such as dataflow, half-perimeter wire length (HPWL), and regularity, aligning with post-MP workflow metrics to enhance power, performance, and area (PPA). To effectively optimize these diverse objectives simultaneously, MAD-EA employs a multi-mask strategy with the Dataflow Mask, HPWL Mask, and Regularity Mask to optimize dataflow, HPWL, and congestion. Experiments show that MAD-EA improves HPWL by up to 33.00% and congestion by 22.74% compared to the state-of-the-art methods.