Abstract
This paper presents a macrocell placement constraint and overlap removal methodology using an improved particle swarm optimization (PSO). Several techniques have been proposed to improve PSO, such as methods to prevent the floorplan from falling into the local minimum and to assist in finding the global minimum. The proposed method can deal with various kinds of placement constraints and can process them simultaneously. Experiments employing MCNC and GSRC benchmarks show the difference in the efficiency and robustness of proposed method in the exploration for more optimal solutions through restricted placement and overlap removal compared with other methods. The proposed approach exhibits rapid convergence and leads to more optimal solutions than other related approaches; furthermore, it displays efficient packing with all the constraints satisfied.
Highlights
VLSI floorplanning is an important aspect in chip design
Vijayan and Tsay [7] proposed the topological overlap removal method, an approach that removes a redundant edge from two critical paths and repeats the process continuously until it makes a significant improvement on the layout area
The recent approach, conducted by Alupoaei and Katkoori, proposed a macrocell overlap removal algorithm that was based on the ant colony optimization (ACO) method [9]
Summary
VLSI floorplanning is an important aspect in chip design. It involves the placement of a set of rectangular circuit modules (macrocells) on a chip to minimize the total area and the total interconnecting wire length and without overlap between two modules since larger chip sizes increase production cost while longer wire lengths increase power consumption and decrease system performance. The recent approach, conducted by Alupoaei and Katkoori, proposed a macrocell overlap removal algorithm that was based on the ant colony optimization (ACO) method [9]. Different approaches are used to handle the different kinds of constraints, but there are no unified methods that can handle all constraints simultaneously As opposed to these previously mentioned methods,this paper utilizes particle swarm optimization (PSO) with an overlap detection and removal mechanism to search for the optimal placement solution.
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