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Dynamic Dataflow Graphs

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review

Details

Original languageEnglish
Title of host publicationHandbook of Signal Processing Systems
PublisherSpringer
Pages1173-1210
ISBN (Electronic)978-3-319-91734-4
ISBN (Print)978-3-319-91733-7
DOIs
Publication statusPublished - 2019
Publication typeA3 Part of a book or another research book

Abstract

Much of the work to date on dataflow models for signal processing system design has focused on decidable dataflow models. This chapter reviews more general dataflow modeling techniques targeted to applications that include dynamic dataflow behavior. The complexity in such applications demands for increased degrees of agility and flexibility in dataflow models. With the application of dataflow techniques addressing these challenges, interest in classes of more general dataflow models has risen correspondingly. 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. The dynamic dataflow models covered in this chapter are Boolean Dataflow, CAL, Parameterized Dataflow, Enable-Invoke Dataflow, Scenario-Aware Dataflow, and Dynamic Polyhedral Process Networks.

Publication forum classification

Field of science, Statistics Finland