先进约束下具有高精度单元移动的鲁棒全局路由引擎

Ziran Zhu, Fuheng Shen, Yangjie Mei, Zhipeng Huang, Jianli Chen, Jun-Zhi Yang
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引用次数: 0

摘要

放置和布线通常被定义为两个独立的问题,以减少设计的复杂性。然而,这种分而治之的方法不可避免地会导致解决方案质量的下降,因为放置和路由的相关性/目标并不完全一致。此外,由于先进的电路设计施加了各种约束(例如,时序,R/C特性,电压面积等),在满足先进约束的同时弥合放置和布线之间的差距变得更加具有挑战性。在本文中,我们开发了一个鲁棒的全局路由引擎,在先进的约束条件下具有高精度的单元移动,以缩小差距并改进路由解决方案。我们首先提出了一种基于固定位置的路由优化技术,以获得收敛的路由结果,为后续的细胞运动提供更准确的信息。为了实现对细胞运动的快速和高精度的位置预测,我们构建了一个考虑复杂约束/目标(例如路由方向和基于层的功耗)的查找表(LUT),并基于LUT为每个细胞生成时序驱动的增益图。最后,在预测的基础上,我们提出了一种细胞移动和簇移动交替的方案,然后是部分撕裂和重路由,以优化路由解决方案。在ICCAD 2020竞赛基准上的实验结果表明,我们的算法在所有已发表的作品中获得了最好的总分。与ICCAD 2021竞赛冠军相比,在ICCAD 2021竞赛基准上的实验结果表明,我们的算法在更短的运行时间内获得了更好的解质量。
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A Robust Global Routing Engine with High-accuracy Cell Movement under Advanced Constraints
Placement and routing are typically defined as two separate problems to reduce the design complexity. However, such a divide-and-conquer approach inevitably incurs the degradation of solution quality due to the correlation/objectives of placement and routing are not entirely consistent. Besides, with various constraints (e.g., timing, R/C characteristic, voltage area, etc.) imposed by advanced circuit designs, bridging the gap between placement and routing while satisfying the advanced constraints has become more challenging. In this paper, we develop a robust global routing engine with high-accuracy cell movement under advanced constraints to narrow the gap and improve the routing solution. We first present a routing refinement technique to obtain the convergent routing result based on fixed placement, which provides more accurate information for subsequent cell movement. To achieve fast and high-accuracy position prediction for cell movement, we construct a lookup table (LUT) considering complex constraints/objectives (e.g., routing direction and layer-based power consumption), and generate a timing-driven gain map for each cell based on the LUT. Finally, based on the prediction, we propose an alternating cell movement and cluster movement scheme followed by partial rip-up and reroute to optimize the routing solution. Experimental results on the ICCAD 2020 contest benchmarks show that our algorithm achieves the best total scores among all published works. Compared with the champion of the ICCAD 2021 contest, experimental results on the ICCAD 2021 contest benchmarks show that our algorithm achieves better solution quality in shorter runtime.
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