Cost Minimization with HPDFG and Data Mining for Heterogeneous DSP

  • Authors:
  • Jian-Wei Niu;Meikang Qiu;Xiaofei Wang;Jiayin Li;Gang Wu;Tianzhou Chen

  • Affiliations:
  • State Key Laboratory of Software Development Environment, Beihang University, Beijing, China 100191;Department of Electrical and Computer Engineering, University of Kentucky, Lexington, USA 40506;School of Computer Science and Engineering, Seoul National University, Seoul, Korea 151-742;Department of Electrical and Computer Engineering, University of Kentucky, Lexington, USA 40506;School of Software, Shanghai Jiao Tong University, Shanghai, People's Republic of China 200030;College of Computer Science, Zhejiang University, Hangzhou, People's Republic of China 310027

  • Venue:
  • Journal of Signal Processing Systems
  • Year:
  • 2012

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Abstract

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.