An RSC-Based Genetic Algorithm for Fixed-Outline Soft Module Floorplanning

Abstract

Floorplanning is a pivotal step in the early stages of integrated circuit physical design. In modern design, the outline of the chip is typically determined before floorplanning and each module must be placed within the specified fixed outline. However, when dealing with soft modules, the task becomes even more complex and challenging as it requires simultaneous determination of both position and shape. This paper proposes a genetic algorithm based on random subtree crossover to address the floorplanning problem of soft modules under fixed-outline constraints. First, a slicing tree is employed to represent the floorplan solution due to its advantage in handling the flexibility of soft modules. A three-step random subtree crossover (RSC) is proposed to generate feasible descendants specifically for slicing tree representation. Second, we adopt three types of perturbations as mutation operators and determine the probability of selecting a mutation operator on account of its empirical performance to improve the effectiveness of mutation. Then, to enhance the genetic algorithm’s rate of convergence, a step named insert after deletion (IAD) is incorporated into the evolutionary process to eliminate operator nodes with large dead spaces. Finally, the algorithm is verified by employing two standard circuits, MCNC and GSRC. The test consequences show that the proposed algorithm realized a 100% success rate across various aspect ratios while surpassing previous works in terms of wire length performance.