Abstract
Much of the work to date on dataflow models for signal processing system design has focused decidable dataflow models that are best suited for onedimensional signal processing. In this chapter, we review more general dataflow modeling techniques that are targeted to applications that include multidimensional signal processing and dynamic dataflow behavior. As dataflow techniques are applied to signal processing systems that are more complex, and demand increasing degrees of agility and flexibility, these classes of more general dataflow models are of correspondingly increasing interest. We begin with a discussion of two dataflow modeling techniques - multi-dimensional synchronous dataflow and windowed dataflow - that are targeted towards multidimensional signal processing applications. We then provide a motivation for dynamic dataflow models of computation, and review a number of specific methods that have emerged in this class of models. Our coverage of dynamic dataflowmodels in this chapter includes Boolean dataflow, the stream-based function model, CAL, parameterized dataflow, and enable-invoke dataflow.
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