Wireless embedded applications have stringent temporal constraints. The frame arrival rate imposes a throughput requirement that must be satisfied. These applications are often dynamic and streaming in nature. The FSM-based Scenario-Aware Dataflow (FSM-SADF) model of computation (MoC) has been proposed to model such dynamic streaming applications. FSM-SADF splits a dynamic system into a set of static modes of operation, called scenarios. Each scenario is modeled by a Synchronous Dataflow (SDF) graph. The possible scenario transitions are specified by a finite-state machine (FSM). FSM-SADF allows a more accurate design-time analysis of dynamic streaming applications, capitalizing on the analysability of SDF. However, existing FSM-SADF analysis techniques assume 1) scenarios are self-timed bounded, for which strong-connectedness is a sufficient condition, and 2) inter-scenario synchronizations are only captured by initial tokens that are common between scenarios. These conditions are too restrictive for many real-life applications. In this paper, we lift these restrictive assumptions and introduce a generalized FSM-SADF analysis approach based on the max-plus linear systems theory. We present both exact and conservative worst-case throughput analysis techniques that have varying levels of accuracy and scalability. The analysis techniques are implemented in a publicly available dataflow analysis tool and experimentally evaluated with different wireless applications.
|Title of host publication||Proceedings of the International Conference on Hardware/Software Codesign and System Synthesis 2012, 7-12 october 2012, Tampere, Finland|
|Place of Publication||New York|
|Publisher||Association for Computing Machinery, Inc|
|Publication status||Published - 2012|