Very Large-Scale Integration Floor Planning on FIR and Lattice Filters Design With Multi-Objective Hybrid Optimization

IF 0.8 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of Swarm Intelligence Research Pub Date : 2023-04-20 DOI:10.4018/ijsir.321237
Pushpalatha Pondreti, Babulu Kaparapu
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引用次数: 0

Abstract

Floor planning is indeed an obvious design process in VLSI physical layout since it specifies the dimensions, structure, as well as positions of components upon the chip; in addition, information regarding the overarching silicon area, interlinks, and latency is also provided. VLSI floor planning is an NP-hard issue as the floor plan representations are a crucial component in this process. The intricacy, as well as solution space of the floor plan layout, is influenced by the floorplan visualizations. To tackle the VLSI floor plan challenge, numerous researchers have developed numerous meta-heuristic optimization techniques. The main objective of this work presents a novel multi-objective hybrid optimization method for solving the floor plan optimization issue. Standard DOX and ALO are conceptually combined in the proposed hybrid optimization referred to as Dingo Updated Ant Lion Optimization (DUALO) model. The multi-objectives like wire length, area, and penalty function are taken into consideration.
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基于多目标混合优化的FIR和格型滤波器设计的超大规模集成楼层规划
平面规划确实是VLSI物理布局中一个明显的设计过程,因为它指定了尺寸,结构以及芯片上组件的位置;此外,还提供了有关总体硅面积、互连和延迟的信息。VLSI平面规划是NP-hard问题,因为平面规划表示是该过程中的关键组成部分。平面图布局的复杂性以及解决方案空间都受到平面图可视化的影响。为了解决VLSI平面图的挑战,许多研究人员开发了许多元启发式优化技术。本文的主要目标是提出一种新的多目标混合优化方法来解决平面优化问题。标准DOX和ALO在概念上结合在一起,被称为Dingo更新蚁狮优化(DUALO)模型。考虑了线长、面积、罚函数等多目标。
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来源期刊
International Journal of Swarm Intelligence Research
International Journal of Swarm Intelligence Research COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
2.50
自引率
0.00%
发文量
76
期刊介绍: The mission of the International Journal of Swarm Intelligence Research (IJSIR) is to become a leading international and well-referred journal in swarm intelligence, nature-inspired optimization algorithms, and their applications. This journal publishes original and previously unpublished articles including research papers, survey papers, and application papers, to serve as a platform for facilitating and enhancing the information shared among researchers in swarm intelligence research areas ranging from algorithm developments to real-world applications.
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