Optimizing resource utilization using transformations

A transformational approach aimed at improving the resource utilization in high level synthesis is introduced. The current implementation combines retiming and associativity in a single framework. This combination of transformations results in considerable area improvements, as is amply demonstrated by benchmark examples. A novel learning while searching iterative improvement probabilistic algorithm has been developed and is used to resolve the associated NP-complete combinatorial optimization problem. The effectiveness of the proposed algorithms and the transformations is demonstrated using standard benchmark examples, with the aid of statistical analysis, and through a comparison with estimated minimal bounds. The proposed algorithm has proven to be very effective in reaching the optimal solution as well as in runtime.<>