A new optimization technique for improving resource exploitation and critical path minimization

The paper presents a novel approach to algebraic optimization of data flow graphs in the domain of computationally intensive applications. The presented approach is based upon the paradigm of simulated evolution which has been proven to be a powerful method for solving large nonlinear optimization problems. We introduce a genetic algorithm with a new chromosomal representation of data flow graphs that serves as a basis for preserving the correctness of algebraic transformations and allows an efficient implementation of the genetic operators. Furthermore, we introduce a new class of hardware related transformation rules which for the first time allow to take existing component libraries into account. The efficiency of our method is demonstrated by encouraging experimental results for several standard benchmarks.

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