Properties and Algorithms for Unfolding of Probabilistic Data-flow Graphs

It is known that any selection statement (e.g. if and switch-case statements) in an application is associated with a probability which could either be predetermined by user input or chosen at runtime. Such a statement can be regarded as a computation node whose computation time is represented by a random variable. This paper focuses on iterative applications (containing loops) reecting those uncertainties. Such an application can then be transformed to a probabilistic data-ow graph. Two timing models, the time-invariant and time-variant models, are introduced to characterize the nature of these applications. Since there can be many unfolding factors associated with each of the possible graph outcomes, for the time-invariant model, we propose a means of selecting a constant minimum rate-optimal unfolding factor for unfolding the probabilistic graph. We demonstrate that this factor guarantees the best schedule length. We also suggest a good estimate for choosing an unfolding..

Properties and Algorithms for Unfolding of Probabilistic Data-flow Graphs

It is known that any selection statement (e.g. if and switch-case statements) in an application is associated with a probability which could either be predetermined by user input or chosen at runtime. Such a statement can be regarded as a computation node whose computation time is represented by a random variable. This paper focuses on iterative applications (containing loops) reecting those uncertainties. Such an application can then be transformed to a probabilistic data-ow graph. Two timing models, the time-invariant and time-variant models, are introduced to characterize the nature of these applications. Since there can be many unfolding factors associated with each of the possible graph outcomes, for the time-invariant model, we propose a means of selecting a constant minimum rate-optimal unfolding factor for unfolding the probabilistic graph. We demonstrate that this factor guarantees the best schedule length. We also suggest a good estimate for choosing an unfolding factor for..