A Swarm Random Walk Based Method for the Standard Cell Placement Problem

Abstract

The standard cell placement (SCP) problem is a well-studied placement problem, as it is an important step in the VLSI design process. In SCP, cells are placed on chip to optimize some objectives, such as wirelength or area. The SCP problem is solved using mainly four basic methods: simulated annealing, quadratic placement, min-cut placement, and force-directed placement. These methods are adequate for small chip sizes. Nowadays, chip sizes are very large, and hence, hybrid methods are employed to solve the SCP problem instead of the original methods by themselves. This paper presents a new hybrid method for the SCP problem using a swarm intelligence-based (SI) method, called SwarmRW (swarm random walk), on top of a min-cut based partitioner. The resulting placer, called sPL (swarm placer), was tested on the PEKU benchmark suite and compared with several related placers. The obtained results demonstrate the effectiveness of the proposed approach and show that sPL can achieve competitive performance.

Swarm intelligence to solve the curriculum sequencing problem

“…However, there are other effective SI methods which have not been examined yet for the CS problem. These include particularly SwarmRW, an SI‐based meta‐heuristic introduced for approximating solutions to continuous optimization problems and also the standard cell placement problem (SCP) . SwarmRW has been demonstrated to outperform PSO in solving several continuous optimization test functions.…”

“…It outperformed some of the best SI meta‐heuristic algorithms on well‐known continuous optimization benchmark functions. A discrete version of SwarmRW was also successfully applied to solve the standard cell placement problem . The aim of this research is to examine SwarmRW and ACS to solve the CS problem, which is formulated as a CSP.…”