Cost minimization with HPDFG and Data mining for heterogeneous DSP

Cost minimization and execution-time reduction have become the most important issues in today's real-time embedded system. Meanwhile, for the DSP (Digital Signal Processing) applications running on embedded system, loops inside them are the most critical part for performance optimization. To optimize the loop iteration patterns, we need to schedule the loop execution order. Due to the uncertainties within the execution time of tasks, we model varied execution times of tasks as random variables and propose a novel data graph model, called HPDFG (Heterogeneous Probabilistic Data-Flow Graph) to model DSP applications on embedded systems. A novel algorithm, LSHAPE, is proposed to minimize the cost and satisfy the timing constraints. First of all, we use the data mining methods to estimate the probabilistic distribution of the execution time variables. Second, we rotate the loops in the application to explore different possible execution patterns. Finally, we combine the list-scheduling and the dynamic programming to generate a near-optimal task allocation and a core-mode assignment. Experimental results demonstrate the effectiveness of our algorithm. Our approach can handle loops efficiently.