Scheduling with confidence for probabilistic data-flow graphs

One of the biggest problems in high-level synthesis is to obtain a good schedule without the knowledge of exact computation time of tasks. While the target applications in high-level synthesis are becoming larger a task in the applications such as artificial intelligent systems or interface may have uncertain computation time. In this paper an algorithm to schedule these repetitive tasks and optimize the schedule is presented. A probabilistic data-flow graph is employed to model the problem where each node represents a task associated with the probabilistic computation time and a set of edges represents the dependences between the tasks. A novel polynomial-time probabilistic retiming algorithm for optimizing the graph and an algorithm for computing the optimized schedule, subject to the acceptable probability and resource constraint, are presented. The optimization algorithm also guarantees to give such a short schedule length with a given qualitatively provable, confidence level. The experiments show that the resulting schedule length for a given confidence probability can be significantly reduced