Evolutionary Algorithm Based Exploration of Software Schedules for Digital Signal Processors


The simultaneous exploration of tradeoffs between program memory, data memory and execution time requirements (3D) for DSP (digital signal processing) algorithms in embedded computing environments is a demanding application and example par excellence of a multi-objective optimization problem. In order to solve this problem, two evolutionary algorithms are shown to be successfully applicable for exploring Pareto-optimal solutions. For different well-known target DSP processors, the trade-off fronts are analyzed. The two approaches are quantitatively compared.