Articles
Fradet, Pascal,
Girault, Alain,
Krishnaswamy, Ruby,
Nicollin, Xavier,
Shafiei, A. Publication Date: 2022
ACM Transactions on Embedded Computing Systems (15399087)(1)
Dataflow Models of Computation (MoCs) are widely used in embedded systems, including multimedia processing, digital signal processing, telecommunications, and automatic control. In a dataflow MoC, an application is specified as a graph of actors connected by FIFO channels. One of the first and most popular dataflow MoCs, Synchronous Dataflow (SDF), provides static analyses to guarantee boundedness and liveness, which are key properties for embedded systems. However, SDF and most of its variants lack the capability to express the dynamism needed by modern streaming applications. In particular, the applications mentioned above have a strong need for reconfigurability to accommodate changes in the input data, the control objectives, or the environment. We address this need by proposing a new MoC called Reconfigurable Dataflow (RDF). RDF extends SDF with transformation rules that specify how and when the topology and actors of the graph may be reconfigured. Starting from an initial RDF graph and a set of transformation rules, an arbitrary number of new RDF graphs can be generated at runtime. A key feature of RDF is that it can be statically analyzed to guarantee that all possible graphs generated at runtime will be consistent and live. We introduce the RDF MoC, describe its associated static analyses, and present its implementation and some experimental results. © 2022 Copyright held by the owner/author(s). Publication rights licensed to ACM.
Fradet, Pascal,
Girault, Alain,
Jamshidian, Leila,
Nicollin, Xavier,
Shafiei, A. Publication Date: 2018
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (03029743)
In this paper, we take into account lossy channels and retransmission protocols in dataflow models of computation (MoCs).Traditional dataflow MoCs cannot easily cope with lossy channels, due to the strict notion of iteration that does not allow the re-emission of lost or damaged tokens. A general dataflow graph with several lossy channels will indeed require several phases, each of them corresponding to a portion of the initial graph’s schedule. Correctly identifying and sequencing these phases is a challenge. We present a translation of a dataflow graph, written in the well-known Synchronous DataFlow (SDF) MoC of Lee and Messerschmitt, but where some channels may be lossy, into the Boolean Parametric DataFlow (BPDF) MoC. © 2018, Springer International Publishing AG, part of Springer Nature.
