Abstract

Much of the work to date on dataflow models for signal processing system design has focused on decidable dataflow models that are best suited for one-dimensional signal processing. This chapter reviews 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 first 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 dataflow models in this chapter includes Boolean dataflow, CAL, parameterized dataflow, enable-invoke dataflow, dynamic polyhedral process networks, scenario aware dataflow, and a stream-based function actor model.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.