Evolutionary algorithms for VLSI multi-objective netlist partitioning

The problem of partitioning appears in several areas ranging from VLSI, parallel programming, to molecular biology. The interest in finding an optimal partition especially in VLSI has been a hot issue in recent years. In VLSI circuit partitioning, the problem of obtaining a minimum cut is of prime importance. With current trends, partitioning with multiple objectives which includes power, delay and area, in addition to minimum cut is in vogue. In this paper, we engineer three iterative heuristics for the optimization of VLSI netlist bi-Partitioning. These heuristics are based on Genetic Algorithms (GAs), Tabu Search (TS) and Simulated Evolution (SimE). Fuzzy rules are incorporated in order to handle the multiobjective cost function. For SimE, fuzzy goodness functions are designed for delay and power, and proved efficient. A series of experiments are performed to evaluate the efficiency of the algorithms. ISCAS-85/89 benchmark circuits are used and experimental results are reported and ...

• Type: Journal
• Year: 2006
• Where: EAAI
• Authors: Sadiq M. Sait, Aiman H. El-Maleh, Raslan H. Al-Abaji
• Keywords: EAAI 2006, Iterative Heuristics, VLSI, VLSI Netlist Bi-partitioning