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首页> 外文期刊>International journal of circuit theory and applications >A multiobjective optimization tool for Very Large Scale Integrated nonslicing floorplanning
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A multiobjective optimization tool for Very Large Scale Integrated nonslicing floorplanning

机译：用于超大规模集成非切片布局的多目标优化工具

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Floorplanning is a vital phase in the design process of Very Large Scale Integrated (VLSI) circuit physical design process. The main objective of floorplanning is to minimize the area and wire length with the fixed-outline constraints. Most of the tools developed so for are using weighted some approach. Hence, these tools suffer from weights assignment and undesirable bias toward particular objective. A tailor-made multiobjective optimization tool could overcome this issue.In this article, we propose a new multiobjective optimization technique named self adaptive B~*tree coded Archived Multiobjective Simulated Annealing Algorithm (AMOSA) and implemented to solve the VLSI nonslicing floorplanning problem. The proposed model provides choices from among different trade-off solutions. The self adaptive B~*tree coded AMOSA combines the novel cooling schedule, B~*tree encoding, improved neighborhood search procedure, self adaptive local search, and the AMOSA. In B~*tree coded AMOSA, the solution is represented using a B~*tree. This representation causes a reduction in time and space complexity of AMOSA. The B~*tree coded AMOSA is further improved with a novel cooling schedule, a self adaptive local search mechanism, and an improved neighborhood search procedure, resulting in further reduction of computational time and improvement in exploration capability. The FastSA, B~*tree coded AMOSA, and the self adaptive B~*tree coded AMOSA are tested with Microelectronics Center of North Carolina (MCNC) benchmarks. The results are compared and validated. The proposed method shows 59.8% improvement in the computational time for ami49 without changing the system quality.
机译：在超大规模集成电路（VLSI）电路物理设计过程的设计过程中，布局规划至关重要。布局规划的主要目的是在固定轮廓约束的情况下最小化面积和电线长度。为此开发的大多数工具都在使用加权方法。因此，这些工具遭受权重分配和对特定目标的不期望的偏差。量身定制的多目标优化工具可以解决这个问题。在本文中，我们提出了一种新的多目标优化技术，称为自适应B〜*树编码的存档多目标模拟退火算法（AMOSA），并已实现，以解决VLSI非切片布局规划问题。所提出的模型提供了不同折衷解决方案之间的选择。自适应B〜* tree编码的AMOSA结合了新颖的冷却时间表，B〜* tree编码，改进的邻域搜索过程，自适应局部搜索和AMOSA。在B〜*树编码的AMOSA中，解决方案使用B〜*树表示。这种表示导致AMOSA的时间和空间复杂度降低。 B *树编码的AMOSA通过新颖的冷却时间表，自适应局部搜索机制和改进的邻域搜索程序得到了进一步改进，从而进一步减少了计算时间并提高了勘探能力。 FastSA，B〜* tree编码的AMOSA和自适应B〜* tree编码的AMOSA已通过北卡罗来纳州微电子中心（MCNC）的基准测试。比较结果并验证。所提出的方法在不改变系统质量的情况下，ami49的计算时间缩短了59.8％。

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