"Unfolding Probabilistic Data-Flow Graphs Under Different Timing Models,"

(by S. Tongsima, T. O'Neil, and E. H.-M. Sha) in Proceedings of the 1999 IEEE International Conference On Acoustics, Speech, and Signal Processing, Phoenix, Arizona, March 1999, Vol 4, pp.1889-1892.



  It is known that in many applications, because of selection statements, e.g., if­statement, the computation time of a node can be represented by a random variable. This paper focuses on any iterative application (containing loops) reflecting those uncertainties. Such an application can then be transformed to a probabilistic data­flow graph. A challenging problem is to derive graph transformation techniques which can produce a good schedule. This paper introduces two timing models, the time­invariant and time­ variant models, to characterize the nature of these applications. Furthermore, for the time­invariant model, we propose a means of selecting a minimum rate-optimal unfolding factor which guarantees the best schedule length. We also propose a good estimation for choosing an unfolding factor for a graph under the time­variant model.


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