Date of Award

28-5-2024

Document Type

Thesis

School

School of Computing

First Advisor

Dr.R.Venkatesan

Keywords

Multi-Objective Metaheuristic Algorithm, VLSI Floor Planning, Ant Colony Optimization, Firefly Optimization, Placement Constraints

Abstract

VLSI floorplanning is a key design step that determines the optimal placement of circuit modules to minimize chip area, wire length, and heat generation. Existing swarm intelligence–based metaheuristics improve area and wire length but often ignore thermal effects.

To address this, the Multi-Objective Firefly Optimization–based Floorplanning (MOFO-FP) technique is introduced, using a Heat-Aware Firefly Optimization (HAFO) algorithm that minimizes heat, space, and wire length under fixed outline constraints. Each firefly represents a floorplan, with brightness indicating solution quality; dimmer fireflies move toward brighter ones to find optimal placements.

A second method, the Hybridized Multicriteria Ant Colony and Firefly Optimization (HMAC-FO), combines ACO and Firefly Optimization. ACO generates optimized initial populations for faster convergence, achieving reductions of 3.48% in area, 0.64% in wire length, and 3.33% in temperature on MCNC benchmarks.

The approach also handles placement constraints, both absolute (preplace, range, boundary) and relative (alignment, abutment, clustering), using horizontal and vertical constraint graphs for efficient constraint satisfaction.

Overall, the proposed hybrid bio-inspired multi-objective optimization algorithm achieves better chip performance by jointly minimizing area, wire length, and heat while meeting all placement constraints.

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