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Social learning discrete Particle Swarm Optimization based two-stage X-routing for IC design under Intelligent Edge Computing architecture
Applied Soft Computing ( IF 8.7 ) Pub Date : 2021-02-24 , DOI: 10.1016/j.asoc.2021.107215
Genggeng Liu , Xiaohua Chen , Ruping Zhou , Saijuan Xu , Yeh-Cheng Chen , Guolong Chen

One of the core features of Intelligent Edge Computing (IEC) is real-time decision making, therefore low delay is more important for IC design under IEC architecture. And in very large scale integration routing, wirelength is one of the most important indexes affecting the final delay of the IC design. Therefore, this paper introduces the X-routing with more potential for wirelength optimization and the Steiner Minimum Tree (SMT), which is the best routing model in multi-terminal nets. Then, based on Particle Swarm Optimization (PSO) technique which has the strong global optimization ability in Soft Computing, an effective Two-Stage X-routing Steiner minimum tree construction algorithm is proposed. The proposed algorithm is divided into two stages: social learning discrete PSO searching and wirelength reduction. In the first stage, two excellent strategies are proposed to maintain a good balance between exploration and exploitation capabilities of the PSO technique: (1) Chaotic decreasing inertia weight combined with mutation operator is set to enhance the exploration capability. (2) A new social learning approach combined with crossover operator is designed to ensure the diverse evolution of the swarm while maintaining the exploitation capability. In the second stage, a strategy based on local topology optimization is proposed to further reduce the length of X-routing Steiner tree. Experiments show that the proposed algorithm can achieve the best wirelength optimization and has a strong stability, especially for large-scale SMT problem, so as to better satisfy the demand of low delay of IC design under IEC architecture.



中文翻译:

智能边缘计算架构下基于社交学习离散粒子群优化的两阶段X路由IC设计

智能边缘计算(IEC)的核心功能之一是实时决策,因此,低延迟对于IEC体系结构下的IC设计更为重要。在大规模集成布线中,线长是影响IC设计最终延迟的最重要指标之一。因此,本文介绍了具有更大潜力进行线长优化的X路由和Steiner最小树(SMT),它是多终端网络中的最佳路由模型。然后,基于在软计算中具有强大的全局优化能力的粒子群优化(PSO)技术,提出了一种有效的两阶段X路由Steiner最小树构造算法。该算法分为两个阶段:社会学习离散PSO搜索线长减少。在第一阶段,提出了两种出色的策略来保持PSO技术的勘探和开发能力之间的良好平衡:(1)设置降低混沌惯性权重并结合变异算子以提高勘探能力。(2)设计了一种新的结合跨界算子的社会学习方法,以在保证开发能力的同时,确保群体的多样化发展。在第二阶段,提出了一种基于局部拓扑优化的策略,以进一步减少X路由Steiner树的长度。实验表明,该算法可以实现最佳的线长优化,并且具有较强的稳定性,特别是对于大规模的SMT问题,可以更好地满足IEC架构下IC设计的低延迟需求。

更新日期:2021-02-28
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