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This paper presents an efficient search method for a scheduling and module selection problem using multiple supply voltages so as to minimize dynamic energy consumption under time and area constraints. The proposed algorithm is based on a genetic algorithm so that it can find near-optimal solutions in a short time for large-size problems. n efficient search can be achieved by crossover that prevents generating nonvalid individuals and a local search is also utilized in the algorithm. Experimental results for large-size problems with 1,000 operations demonstrate that the proposed method can achieve significant energy reduction up to 50 percent and can find a near-optimal solution (within 2.8 percent from the lower bound of optimal solutions) in 10 minutes. On the other hand, the ILP-based method cannot find any feasible solution in one hour for the large-size problem, even if a state-of-art mathematical programming solver is used.
Automatic synthesis, scheduling, module selection, data-path design.

M. Kameyama, M. Hariyama and T. Aoyama, "Genetic Approach to Minimizing Energy Consumption of VLSI Processors Using Multiple Supply Voltages," in IEEE Transactions on Computers, vol. 54, no. , pp. 642-650, 2005.
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