ANT COLONY OPTIMIZATION FOR VLSI FLOORPLANNING WITH CLUSTERING CONSTRAINTS

ABSTRACT

In this study the very large scale integration (VLSI) floorplanning problem with clustering constraints and the layout area as the minimization criterion is considered. An algorithm, which is based on the primary principles of ant colony optimization (ACO), to solve this problem is presented. This ACO-based algorithm employs two different types of pheromone trails as the communication media among artificial ants to effectively guide them to cooperatively construct a high quality floorplan. On the basis of the characteristics of ACO, moreover, an encoding scheme, which is referred to as dynamic junction list (DJL), is proposed to represent the geometric relationships between circuit modules for a floorplan. Experimental results using the Microelectronics Center of North Carolina (MCNC) benchmarks demonstrate the effectiveness of the proposed algorithm.

Keywords:

• very large scale integration floorplanning
• clustering constraints
• combinatorial optimization problems
• ant colony optimization