{"ID":2843291,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.08054","arxiv_id":"2511.08054","title":"Re$^{\\text{2}}$MaP: Macro Placement by Recursively Prototyping and Packing Tree-based Relocating","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.","short_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 datafl...","url_abs":"https://arxiv.org/abs/2511.08054","url_pdf":"https://arxiv.org/pdf/2511.08054v1","authors":"[\"Yunqi Shi\",\"Xi Lin\",\"Zhiang Wang\",\"Siyuan Xu\",\"Shixiong Kai\",\"Yao Lai\",\"Chengrui Gao\",\"Ke Xue\",\"Mingxuan Yuan\",\"Chao Qian\",\"Zhi-Hua Zhou\"]","published":"2025-11-11T09:56:10Z","proceeding":"cs.AR","tasks":"[\"cs.AR\",\"cs.CV\",\"eess.SY\"]","methods":"[]","has_code":false,"code_links":[{"ID":607197,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2843291,"paper_url":"https://arxiv.org/abs/2511.08054","paper_title":"Re$^{\\text{2}}$MaP: Macro Placement by Recursively Prototyping and Packing Tree-based Relocating","repo_url":"https://github.com/lamda-bbo/Re2MaP","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}