Two-Stage Competitive Particle Swarm Optimization Based Timing-Driven X-Routing for IC Design Under Smart Manufacturing
Two-Stage Competitive Particle Swarm Optimization Based Timing-Driven X-Routing for IC Design Under Smart Manufacturing
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智能制造下基于时序驱动的X-routing的两阶段竞争粒子群优化IC设计
DOI：
10.1145/3531328
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发表时间：
2022-04
影响因子：
2.5
通讯作者：
Guolong Chen
中科院分区：
文献类型：
--
作者：
Genggeng Liu;Ruping Zhou;Saijuan Xu;Yuhan Zhu;Wenzhong Guo;Yeh-Cheng Chen;Guolong Chen
随着时间延迟成为芯片性能的关键问题，智能制造下的IC设计迫切需要优化延迟。Steiner最小树作为多端网络的最佳连接模型，其线长和最大源到宿路径长度是路由时延的决定性因素。另外，考虑到X路由可以最大限度地利用路由资源，提出了一种基于两阶段竞争粒子群优化的时间驱动X路由Steiner最小树（TD-XSMT）算法。本文利用多目标粒子群优化算法，重新设计了算法框架，提高了算法性能。首先，提出了一种两阶段学习策略，通过学习边结构和伪Steiner点选择来平衡粒子的探索和利用能力。特别是在第二阶段，混合交叉策略的设计，以保证收敛质量。其次，采用竞争机制选择粒子学习对象，增强多样性。最后，根据离散TD-XSMT问题的特点，采用遗传算法中的变异和交叉算子对算法进行有效的离散化。实验结果表明，TSCPSO-TD-XSMT可以在线路长度和最大源到宿路径长度之间获得平滑的折衷，并实现出色的定时延迟优化。
As timing delay becomes a critical issue in chip performance, there is a burning desire for IC design under smart manufacturing to optimize the delay. As the best connection model for multi-terminal nets, the wirelength and the maximum source-to-sink pathlength of the Steiner minimum tree are the decisive factors of timing delay for routing. In addition, considering that X-routing can get the utmost out of routing resources, this article proposes a Timing-Driven X-routing Steiner Minimum Tree (TD-XSMT) algorithm based on two-stage competitive particle swarm optimization. This work utilizes the multi-objective particle swarm optimization algorithm and redesigns its framework, thus improving its performance. First, a two-stage learning strategy is presented, which balances the exploration and exploitation capabilities of the particle by learning edge structures and pseudo-Steiner point choices. Especially in the second stage, a hybrid crossover strategy is designed to guarantee convergence quality. Second, the competition mechanism is adopted to select particle learning objects and enhance diversity. Finally, according to the characteristics of the discrete TD-XSMT problem, the mutation and crossover operators of the genetic algorithm are used to effectively discretize the proposed algorithm. Experimental results reveal that TSCPSO-TD-XSMT can obtain a smooth trade-off between wirelength and maximum source-to-sink pathlength, and achieve distinguished timing delay optimization.
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DOI：
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