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Volume 47 Issue 7
Jul.  2025
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ZHU Yanzhen, YAN Haopeng, CAI Shuting, GAO Peng. Wire Length Driven Tension Refine Based Macro Placer[J]. Journal of Electronics & Information Technology, 2025, 47(7): 2396-2404. doi: 10.11999/JEIT241079
Citation: ZHU Yanzhen, YAN Haopeng, CAI Shuting, GAO Peng. Wire Length Driven Tension Refine Based Macro Placer[J]. Journal of Electronics & Information Technology, 2025, 47(7): 2396-2404. doi: 10.11999/JEIT241079

Wire Length Driven Tension Refine Based Macro Placer

doi: 10.11999/JEIT241079 cstr: 32379.14.JEIT241079
Funds:  Guangdong S&T Programme (2022B0701180001)
  • Received Date: 2024-12-06
  • Rev Recd Date: 2025-03-29
  • Available Online: 2025-04-11
  • Publish Date: 2025-07-22
  •   Objective  With the introduction of reuse methodologies in integrated circuit design, the utilization of macro cells in Very Large Scale Integration (VLSI) has significantly increased. However, the considerable size difference between macro cells and standard cells presents a significant challenge for circuit placers. This study proposes a novel macro placer, WIMPlace, based on tension fine-tuning and wirelength-driven approaches. The aim is to address issues such as density imbalance and degradation of solution quality observed in existing mixed-size placers, thereby providing a more effective solution for VLSI design.  Methods  The proposed method in this paper consists of four stages: preprocessing, pre-placement, macro cell fine-tuning, and macro legalization. Initially, a weight-based partitioning approach is employed to group standard cells with macro into supersets, addressing density issues during the initial placement (Section 3.1). In the pre-placement stage, the DREAMPlace 2.0 tool is used for placing standard cells, and the initial positions of macro cells are determined based on the locations of these clusters (Section 3.2). A local tension model, inspired by the principle of surface tension in liquids, is then adopted to fine-tune the positions of macros, ensuring that connections between standard cells and macros are as compact as possible (Section 3.3, Fig. 2). Finally, a constraint graph-based macro legalization strategy is applied to prevent overlaps between macros (Section 3.4, Fig. 3).  Results and Discussions  Experimental results demonstrate that the WIMPlace achieves exceptional performance on the MMS benchmark, outperforming other advanced mixed-size placers, such as ePlace-MS and DREAMPlace 4.0. Specifically, in 15 out of 16 cases, it achieved the shortest wirelength, with average reductions of 4.31% and 2.39%, respectively (Section 4, Table 2). Additionally, WIMPlace exhibits excellent solution stability, particularly showing a linear increase in runtime as the number of cells increases (Section 4, Fig. 4), indicating that the algorithm not only optimizes wirelength effectively but also demonstrates high computational efficiency. Notably, in the newblue3 case, despite the macro cells occupying a significant portion of the chip area, WIMPlace still demonstrated strong adaptability.  Conclusions  In summary, WIMPlace, as proposed in this paper, is an efficient macro cell placer that achieves gradual fine-tuning optimization of macro cells by combining gradient field movements based on a surface tension analogy and employing preprocessing techniques to balance macros with their associated standard cells. Compared to existing mixed-size placers, WIMPlace demonstrates superior performance across multiple key metrics, particularly in wirelength optimization. Future work could focus on integrating additional design objectives, such as timing, congestion, and thermal management, to enhance the applicability and flexibility of WIMPlace. This study provides new perspectives and technical approaches for VLSI design.
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  • [1]
    CHENG C K, KAHNG A B, KUNDU S, et al. Assessment of reinforcement learning for macro placement[C]. The 2023 International Symposium on Physical Design, New York, USA, 2023: 158–166. doi: 10.1145/3569052.3578926.
    [2]
    LANIUS C, LOU Jie, LOH J, et al. Automatic generation of structured macros using standard cells‒application to CIM[C]. The 2023 IEEE/ACM International Symposium on Low Power Electronics and Design, Vienna, Austria, 2023: 1–6. doi: 10.1109/ISLPED58423.2023.10244608.
    [3]
    YU Shenglu and DU Shimin. VLSI floorplanning algorithm based on reinforcement learning with obstacles[M]. SAMSONOVICH A V and LIU Tingting. Biologically Inspired Cognitive Architectures 2023. Cham: Springer, 2024: 1034–1043. doi: 10.1007/978-3-031-50381-8_110.
    [4]
    CHANG Y C, CHANG Yaowen, WU Guangming, et al. B*-Trees: A new representation for non-slicing floorplans[C]. The 37th Annual Design Automation Conference, Los Angeles, USA, 2000: 458–463. doi: 10.1145/337292.337541.
    [5]
    HONG Xianlong, HUANG Gang, CAI Yici, et al. Corner block list: An effective and efficient topological representation of non-slicing floorplan[C]. IEEE/ACM International Conference on Computer Aided Design, San Jose, USA, 2000: 8–12. doi: 10.1109/ICCAD.2000.896442.
    [6]
    YAN J Z, VISWANATHAN N, and CHU C. Handling complexities in modern large-scale mixed-size placement[C]. Proceedings of the 46th Annual Design Automation Conference, San Francisco, USA, 2009: 436–441. doi: 10.1145/1629911.1630028.
    [7]
    KIM M C and MARKOV I L. ComPLx: A competitive primal-dual Lagrange optimization for global placement[C]. The 49th Annual Design Automation Conference, San Francisco, USA, 2012: 747–752. doi: 10.1145/2228360.2228496.
    [8]
    LIN Tao, CHU C, SHINNERL J R, et al. POLAR: Placement based on novel rough legalization and refinement[C]. 2013 IEEE/ACM International Conference on Computer-Aided Design, San Jose, USA, 2013: 357–362. doi: 10.1109/ICCAD.2013.6691143.
    [9]
    CHEN T C, JIANG Zhewei, HSU T C, et al. NTUplace3: An analytical placer for large-scale mixed-size designs with preplaced blocks and density constraints[J]. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2008, 27(7): 1228–1240. doi: 10.1109/TCAD.2008.923063.
    [10]
    CHAN T F, CONG J, SHINNERL J R, et al. mPL6: Enhanced multilevel mixed-size placement[C]. The 2006 International Symposium on Physical Design, San Jose, USA, 2006: 212–214. doi: 10.1145/1123008.1123055.
    [11]
    LU Jingwei, ZHUANG Hao, CHEN Pengwen, et al. ePlace-MS: Electrostatics-based placement for mixed-size circuits[J]. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2015, 34(5): 685–698. doi: 10.1109/TCAD.2015.2391263.
    [12]
    AGNESINA A, RAJVANSHI P, YANG Tian, et al. AutoDMP: Automated DREAMPlace-based macro placement[C]. The 2023 International Symposium on Physical Design, 2023: 149–157. doi: 10.1145/3569052.3578923.
    [13]
    YU Tao, GAO Peng, WANG Fei, et al. Non‐overlapping placement of macro cells based on reinforcement learning in chip design[J]. International Journal of Circuit Theory and Applications, 2025, 53(2): 1159–1170. doi: 10.1002/cta.4235.
    [14]
    SCHLAG S, HEUER T, GOTTESBÜREN L, et al. High-quality hypergraph partitioning[J]. ACM Journal of Experimental Algorithmics, 2022, 27: 1.9. doi: 10.1145/3529090.
    [15]
    CHEON Y and WONG D F. Design hierarchy guided multilevel circuit partitioning[C]. The 2002 International Symposium on Physical Design, San Diego, USA, 2002: 30–35. doi: 10.1145/505388.505398.
    [16]
    LIN Yibo, PAN D Z, REN Haoxing, et al. DREAMPlace 2.0: Open-source GPU-accelerated global and detailed placement for large-scale VLSI designs[C]. 2020 China Semiconductor Technology International Conference, Shanghai, China, 2020: 1–4. doi: 10.1109/CSTIC49141.2020.9282573.
    [17]
    CONG J and XIE Min. A robust detailed placement for mixed-size IC designs[C]. The 2006 Asia and South Pacific Design Automation Conference, Yokohama, Japan, 2006: 188–194. doi: 10.1145/1118299.1118353.
    [18]
    VISWANATHAN N, PAN Min, and CHU C. FastPlace 3.0: A fast multilevel quadratic placement algorithm with placement congestion control[C]. 2007 Asia and South Pacific Design Automation Conference, Yokohama, Japan, 2007: 135–140. doi: 10.1109/ASPDAC.2007.357975.
    [19]
    HSU M K and CHANG Yaowen. Unified analytical global placement for large-scale mixed-size circuit designs[J]. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2012, 31(9): 1366–1378. doi: 10.1109/TCAD.2012.2193582.
    [20]
    LIAO Peiyu, GUO Dawei, GUO Zizheng, et al. DREAMPlace 4.0: Timing-driven placement with momentum-based net weighting and Lagrangian-based refinement[J]. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2023, 42(10): 3374–3387. doi: 10.1109/TCAD.2023.3240132.
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