Timing-Driven X-architecture Steiner Minimum Tree Construction Based on Social Learning Multi-Objective Particle Swarm Optimization

Abstract

The construction of timing-driven Steiner minimum tree is a critical issue in VLSI routing design. Meanwhile, since the interconnection model of X-architecture can make full use of routing resources compared to the traditional Manhattan architecture, constructing a Timing-Driven X-architecture Steiner Minimum Tree (TDXSMT) is of great significance to improving routing performance. In this paper, an efficient algorithm based on Social Learning Multi-Objective Particle Swarm Optimization (SLMOPSO) is proposed to construct a TDXSMT with minimizing the maximum source-to-sink pathlength. An X-architecture Prim-Dijkstra model is presented to construct an initial Steiner tree which can optimize both the wirelength and the maximum source-to-sink pathlength. In order to find a better solution, an SLMOPSO method based on the nearest and best select strategy is presented to improve the global exploration capability of the algorithm. Besides, the mutation and crossover operators are utilized to achieve the discrete particle update process, thereby better solving the discrete TDXSMT problem. The experimental results indicate that the proposed algorithm has an excellent trade-off between the wirelength and maximum source-to-sink pathlength of the routing tree and can greatly optimize the timing delay.