End-to-End Latency Analysis of Dataflow Scenarios Mapped Onto Shared Heterogeneous Resources

The design of embedded wireless and multimedia applications requires temporal analysis to verify if real-time constraints such as throughput and latency are met. This paper presents a design-time analytical approach to derive a conservative upper bound to the maximum end-to-end latency of a streaming application. Existing analytical approaches often assume static application models, which cannot cope with the data-dependent execution of dynamic streaming applications. Consequently, they give overly pessimistic upper bounds. In this paper, we use an expressively richer dataflow model of computation as an application model. The model supports adaptive applications that change their graph structure, execution times, and data rates, depending on their mode of operation, or scenario. We first formalize the latency analysis problem in the presence of dynamically switching scenarios. We characterize each scenario with a compact matrix in (max, +) algebra using a symbolic execution of one graph iteration. The resulting matrices are then composed to derive a bound to the end-to-end latency under a periodic source. Aperiodic sources such as sporadic streams can be analyzed by reduction to a periodic reference. We demonstrate the applicability of the technique with dataflow models from the wireless application domain. Moreover, the method is illustrated with a tradeoff analysis in resource reservation under a throughput constraint. The evaluation shows that the approach has a low runtime, which enables it to be effectively integrated in multiprocessor design flows of streaming applications.