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首页> 外文期刊>Computer-Aided Design of Integrated Circuits and Systems, IEEE Transactions on >Efficient Wire Routing and Wire Sizing for Weight Minimization of Automotive Systems
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Efficient Wire Routing and Wire Sizing for Weight Minimization of Automotive Systems

机译:高效的布线和布线尺寸,可最大程度地减轻汽车系统的重量

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As the complexities of automotive systems increase, designing a system is a difficult task that cannot be done manually. In this paper, we focus on wire routing and wire sizing for weight minimization to deal with more and more connections between devices in automotive systems. The wire routing problem is formulated as a minimal Steiner tree problem with capacity constraints, and the location of a Steiner vertex is selected to add a splice which is used to connect more than two wires. We modify the Kou-Markowsky-Berman algorithm to efficiently construct Steiner trees and propose an integer linear programming (ILP) formulation to relocate Steiner vertices and satisfy capacity constraints. The ILP formulation is relaxed to a linear programming (LP) formulation which has the same optimal objective and can be solved more efficiently. Besides wire routing, wire sizing is also performed to satisfy resistance constraints and minimize the total wiring weight. To the best of our knowledge, this is the first work in the literature to formulate the automotive routing problem as a minimal Steiner tree problem with capacity constraints and perform wire routing and wire sizing for weight minimization. An industrial case study shows the effectiveness and efficiency of our algorithm which provides an efficient, flexible, and scalable approach for the design optimization of automotive systems.
机译:随着汽车系统的复杂性增加,设计系统是一项艰巨的任务,无法手动完成。在本文中,我们专注于布线和尺寸调整,以最大程度地减轻重量,以处理汽车系统中越来越多的设备之间的连接。导线布线问题被公式化为具有容量限制的最小Steiner树问题,并且选择了Steiner顶点的位置以添加用于连接多于两条导线的接头。我们修改了Kou-Markowsky-Berman算法以有效地构造Steiner树,并提出了整数线性规划(ILP)公式来重新定位Steiner顶点并满足容量约束。将ILP公式放宽为具有相同最佳目标并可以更有效地求解的线性规划(LP)公式。除布线外,还执行布线工艺来满足电阻约束并最大程度地减小总布线重量。据我们所知,这是文献中将汽车布线问题表述为具有容量限制的最小Steiner树问题并进行布线和布线以实现重量最小化的第一项工作。工业案例研究显示了我们算法的有效性和效率,该算法为汽车系统的设计优化提供了有效,灵活和可扩展的方法。

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