Simulated evolution algorithm for multiobjective VLSI netlist bi-partitioning
In this paper the Simulated Evolution algorithm (SimE) is engineered to solve the optimization problem of multi-objective VLSI netlist bi-partitioning. The multi-objective version of the problem is addressed in which, power dissipation, timing performance, as well as cut-set are optimized while Bala...
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| Other Authors: | , , |
| Format: | article |
| Published: |
2003
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| Online Access: | https://eprints.kfupm.edu.sa/id/eprint/14529/1/14529_1.pdf https://eprints.kfupm.edu.sa/id/eprint/14529/2/14529_2.doc |
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| Summary: | In this paper the Simulated Evolution algorithm (SimE) is engineered to solve the optimization problem of multi-objective VLSI netlist bi-partitioning. The multi-objective version of the problem is addressed in which, power dissipation, timing performance, as well as cut-set are optimized while Balance is taken as a constraint. Fuzzy rules are used in order to design the overall multi-objective cost function that integrates the costs of three objectives in a single overall cost value. Fuzzy goodness functions are designed for delay and power, and proved efficient. A series of experiments are performed to evaluate the efficiency of the algorithm. ISCAS-85/89 benchmark circuits are used and experimental results are reported and compared to earlier algorithms like GA and TS. |
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