Abstract
The timed dataflow model of computation is a useful performance analysis tool for electronic system level design automation and embedded software synthesis. Its determinism gives it strong analyzability properties. Its monotonic temporal behavior provides hard real-time guarantees on throughput and latency. It is expressive enough to cover a large class of applications and platforms. The trend however, in both embedded applications and their platforms is to become more dynamic, reaching the limits of what the model can express and analyze with tight performance guarantees. Scenario-aware dataflow (SADF) allows more dynamism to be expressed, introducing a controlled amount of non-determinism into the model to represent different scenarios of behavior. We investigate so-called weakly consistent graphs in which the scenario changes are not tightly coupled with periods of repetitive behavior of the static dataflow behavior in scenarios as in previous methods. We define the semantics of such graphs in terms of (max , +)-algebra and we introduce a method to analyze throughput using a generalization of (max , +)-automata. We show that weakly-consistent SADF generalizes many of the existing analyzable dynamic dataflow models, such as CSDF, PDF and CFDF and we present an algorithm to convert CSDF graphs to SADF.
Highlights
To develop concurrent embedded software applications and the platforms on which they execute, it is important to be able to efficiently assess whether or not performance requirements will be met
4 Embedded Systems Innovation by TNO, Eindhoven, The Netherlands weakly-consistent Scenario-aware dataflow (SADF) generalizes many of the existing analyzable dynamic dataflow models, such as Cyclo-Static Dataflow (CSDF), Parameterized Dataflow (PDF) and Core Functional Dataflow (CFDF) and we present an algorithm to convert CSDF graphs to SADF
We introduced an exact analysis method for a class of dynamic dataflow graphs, called weakly consistent scenario-aware dataflow in which the behavior may nondeterministically vary according to scenarios of behavior, yet within these scenarios behavior is deterministic and follows the synchronous dataflow paradigm which provides us with powerful analysis techniques
Summary
To develop concurrent embedded software applications and the platforms on which they execute, it is important to be able to efficiently assess whether or not performance requirements will be met. This generalization is important because it allows us to use non-determinism to model dataflow graphs that are not globally synchronous This type of behavior is observed, for instance, in the MP3 example where the file reading front-end operates asynchronously from the sound decoding back-end, i.e., the amount of data that needs to be read to produce a new audio frame may vary, due to data-dependent levels of data reduction. Interleaving of non-deterministic choices of the FSM (which, after unfolding the counters, has 65 states) with the execution of this dataflow graph (792 firings in the largest scenario, ss, as well as pipelining of multiple frames) can lead to state-space explosion in a naive statespace model In this example there are two independent, unsynchronized, sources of non-deterministic behavior.
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