paper-with-me

홈 › Papers

Timing-Driven Global Placement by Efficient Critical Path Extraction

2025-02-28 · Yunqi Shi, Siyuan Xu, Shixiong Kai, Xi Lin, Ke Xue, Mingxuan Yuan, Chao Qian

Timing optimization during the global placement of integrated circuits has been a significant focus for decades, yet it remains a complex, unresolved issue. Recent analytical methods typically use pin-level timing information to adjust net weights, which is fast and simple but neglects the path-based nature of the timing graph. The existing path-based methods, however, cannot balance the accuracy and efficiency due to the exponential growth of number of critical paths. In this work, we propose a GPU-accelerated timing-driven global placement framework, integrating accurate path-level information into the efficient DREAMPlace infrastructure. It optimizes the fine-grained pin-to-pin attraction objective and is facilitated by efficient critical path extraction. We also design a quadratic distance loss function specifically to align with the RC timing model. Experimental results demonstrate that our method significantly outperforms the current leading timing-driven placers, achieving an average improvement of 40.5% in total negative slack (TNS) and 8.3% in worst negative slack (WNS), as well as an improvement in half-perimeter wirelength (HPWL).

📄 PDF Abstract BibTeX arXiv:2503.11674

Code (0)

등록된 구현이 없습니다.

Tasks

GPU

Methods 이 논문이 사용한 방법론

Focus 설명 없음
ALIGN In the ALIGN method, visual and language representations are jointly trained from noisy image alt-text data. The image and text encoders are learned via contrastive loss…

Similar Papers 제목 키워드 기반

ParaGate: Parasitic-Driven Domain Adaptation Transfer Learning for Netlist Performance Prediction

2025-11-28 · Bin Sun, Jingyi Zhou, Jianan Mu, Zhiteng Chao 외 arxiv

In traditional EDA flows, layout-level performance metrics are only obtainable after placement and routing, hindering global optimization at earlier stages. Although some neural-network-based solutions predict layout-lev…

Transfer LearningDomain Adaptation

PPAPlace: Differentiable Cross-Stage Objectives for Chip Placement Optimization

2026-08-13 · Ruogu Chen, Jie Han arxiv

Macro placement significantly affects a chip's post-route performance, power, and area (PPA). Most placement methods optimize half-perimeter wirelength (HPWL) as the primary objective. However, recent benchmarking shows …

TransPlace: Transferable Circuit Global Placement via Graph Neural Network

2025-01-10 · Yunbo Hou, Haoran Ye, Shuwen Yang, Yingxue Zhang 외

Global placement, a critical step in designing the physical layout of computer chips, is essential to optimize chip performance. Prior global placement methods optimize each circuit design individually from scratch. Thei…

Graph Neural Network

Net2: A Graph Attention Network Method Customized for Pre-Placement Net Length Estimation

2020-11-27 · Zhiyao Xie, Rongjian Liang, Xiaoqing Xu, Jiang Hu 외

Net length is a key proxy metric for optimizing timing and power across various stages of a standard digital design flow. However, the bulk of net length information is not available until cell placement, and hence it is…

Graph Attention

PreRoutGNN for Timing Prediction with Order Preserving Partition: Global Circuit Pre-training, Local Delay Learning and Attentional Cell Modeling

2024-02-27 · RuiZhe Zhong, Junjie Ye, Zhentao Tang, Shixiong Kai 외

Pre-routing timing prediction has been recently studied for evaluating the quality of a candidate cell placement in chip design. It involves directly estimating the timing metrics for both pin-level (slack, slew) and edg…

Graph Embedding