Re$^{\text{2}}$MaP: Macro Placement by Recursively Prototyping and Packing Tree-based Relocating

• URL: http://arxiv.org/abs/2511.08054v1
• Date: Wed, 12 Nov 2025 01:37:05 GMT
• Title: Re$^{\text{2}}$MaP: Macro Placement by Recursively Prototyping and Packing Tree-based Relocating
• Authors: Yunqi Shi, Xi Lin, Zhiang Wang, Siyuan Xu, Shixiong Kai, Yao Lai, Chengrui Gao, Ke Xue, Mingxuan Yuan, Chao Qian, Zhi-Hua Zhou,
• Abstract summary: This work introduces the Re$text2$MaP method, which generates expert-quality macro placements.<n>We use DREAMPlace to build a mixed-size placement prototype and obtain reference positions for each macro and cluster.<n>A packing tree-based relocating procedure is then designed to jointly adjust the locations of macro groups and the macros within each group.
• Score: 67.49674976434322
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work introduces the Re$^{\text{2}}$MaP method, which generates expert-quality macro placements through recursively prototyping and packing tree-based relocating. We first perform multi-level macro grouping and PPA-aware cell clustering to produce a unified connection matrix that captures both wirelength and dataflow among macros and clusters. Next, we use DREAMPlace to build a mixed-size placement prototype and obtain reference positions for each macro and cluster. Based on this prototype, we introduce ABPlace, an angle-based analytical method that optimizes macro positions on an ellipse to distribute macros uniformly near chip periphery, while optimizing wirelength and dataflow. A packing tree-based relocating procedure is then designed to jointly adjust the locations of macro groups and the macros within each group, by optimizing an expertise-inspired cost function that captures various design constraints through evolutionary search. Re$^{\text{2}}$MaP repeats the above process: Only a subset of macro groups are positioned in each iteration, and the remaining macros are deferred to the next iteration to improve the prototype's accuracy. Using a well-established backend flow with sufficient timing optimizations, Re$^{\text{2}}$MaP achieves up to 22.22% (average 10.26%) improvement in worst negative slack (WNS) and up to 97.91% (average 33.97%) improvement in total negative slack (TNS) compared to the state-of-the-art academic placer Hier-RTLMP. It also ranks higher on WNS, TNS, power, design rule check (DRC) violations, and runtime than the conference version ReMaP, across seven tested cases. Our code is available at https://github.com/lamda-bbo/Re2MaP.

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