CSF: Fixed-outline Floorplanning Based on the Conjugate Subgradient Algorithm Assisted by Q-Learning

To perform the fixed-outline floorplanning problem efficiently, we propose to solve the original nonsmooth analytic optimization model via the conjugate subgradient algorithm (CSA), which is further accelerated by adaptively regulating the step size with the assistance of Q-learning. The objective for global floorplanning is a weighted sum of the half-perimeter wirelength, the overlapping area and the out-of-bound width, and the legalization is implemented by optimizing the weighted sum of the overlapping area and the out-of-bound width. Meanwhile, we also propose two improved variants for the legalizaiton algorithm based on constraint graphs (CGs). Experimental results demonstrate that the CSA assisted by Q-learning (CSAQ) can address both global floorplanning and legalization efficiently, and the two stages jointly contribute to competitive results on the optimization of wirelength. Meanwhile, the improved CG-based legalization methods also outperforms the original one in terms of runtime and success rate.

Floorplanning of VLSI by Mixed-Variable Optimization

By formulating the floorplanning of VLSI as a mixed-variable optimization problem, this paper proposes to solve it by memetic algorithms, where the discrete orientation variables are addressed by the distribution evolutionary algorithm based on a population of probability model (DEA-PPM), and the continuous coordination variables are optimized by the conjugate sub-gradient algorithm (CSA). Accordingly, the fixed-outline floorplanning algorithm based on CSA and DEA-PPM (FFA-CD) and the floorplanning algorithm with golden section strategy (FA-GSS) are proposed for the floorplanning problems with and without fixed-outline constraint. %FF-CD is committed to optimizing wirelength targets within a fixed profile. FA-GSS uses the Golden Section strategy to optimize both wirelength and area targets. The CSA is used to solve the proposed non-smooth optimization model, and the DEA-PPM is used to explore the module rotation scheme to enhance the flexibility of the algorithm. Numerical experiments on GSRC test circuits show that the proposed algorithms are superior to some celebrated B*-tree based floorplanning algorithms, and are expected to be applied to large-scale floorplanning problems due to their low time complexity.

A Distribution Evolutionary Algorithm for Graph Coloring

Graph Coloring Problem (GCP) is a classic combinatorial optimization problem that has a wide application in theoretical research and engineering. To address complicated GCPs efficiently, a distribution evolutionary algorithm based on population of probability models (DEA-PPM) is proposed. Based on a novel representation of probability model, DEA-PPM employs a Gaussian orthogonal search strategy to explore the probability space, by which global exploration can be realized using a small population. With assistance of local exploitation on a small solution population, DEA-PPM strikes a good balance between exploration and exploitation. Numerical results demonstrate that DEA-PPM performs well on selected complicated GCPs, which contributes to its competitiveness to the state-of-the-art metaheuristics.