ANT COLONY OPTIMIZATION FOR VLSI FLOORPLANNING WITH CLUSTERING CONSTRAINTS

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.

[1]  Vittorio Maniezzo,et al.  The Ant System Applied to the Quadratic Assignment Problem , 1999, IEEE Trans. Knowl. Data Eng..

[2]  Anthony A. Maciejewski,et al.  Task Matching and Scheduling in Heterogenous Computing Environments Using a Genetic-Algorithm-Based Approach , 1997, J. Parallel Distributed Comput..

[3]  Luca Maria Gambardella,et al.  Ant colony system: a cooperative learning approach to the traveling salesman problem , 1997, IEEE Trans. Evol. Comput..

[4]  Pearl Y. Wang,et al.  VLSI placement and area optimization using a genetic algorithm to breed normalized postfix expressions , 2002, IEEE Trans. Evol. Comput..

[5]  Chuan-Wen Chiang,et al.  Ant colony optimization with parameter adaptation for multi-mode resource-constrained project scheduling , 2008, J. Intell. Fuzzy Syst..

[6]  Chang-Tzu Lin,et al.  VLSI floorplanning with boundary constraints using generalized polish expression , 2006 .

[7]  Evangeline F. Y. Young,et al.  Placement constraints in floorplan design , 2004, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

[8]  Evangeline F. Y. Young,et al.  Slicing floorplan with clustering constraint , 2003, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..

[9]  Yoji Kajitani,et al.  Module placement on BSG-structure and IC layout applications , 1996, ICCAD 1996.

[10]  Chuan-Wen Chiang.  Applying an improved fast ant system to DAG scheduling for computational grids , 2008 .

[11]  Hartmut Schmeck,et al.  Ant colony optimization for resource-constrained project scheduling , 2000, IEEE Trans. Evol. Comput..

[12]  Marco Dorigo,et al.  Ant colony optimization theory: A survey , 2005, Theor. Comput. Sci..

[13]  Pinaki Mazumder,et al.  A genetic approach to standard cell placement using meta-genetic parameter optimization , 1990, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..

[14]  Evangeline F. Y. Young,et al.  Slicing floorplans with range constraint , 1999, ISPD '99.

[15]  Shinn-Ying Ho,et al.  An orthogonal simulated annealing algorithm for large floorplanning problems , 2004, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

[16]  Thomas Stützle,et al.  MAX-MIN Ant System , 2000, Future Gener. Comput. Syst..

[17]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[18]  Yoji Kajitani,et al.  VLSI module placement based on rectangle-packing by the sequence-pair , 1996, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..