Worst-case throughput analysis of real-time dynamic streaming applications.

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

Automated extraction of scenario sequences from disciplined dataflow networks.

Analysing deadlock-freedom, boundedness and realtime constraints are crucial steps in the design of embedded streaming applications. Dataflow models of computation are often used to analyse such properties at design-time. To that end, scenario-based dataflow techniques isolate the individual operating scenarios of a dynamic application and analyse the executions of the possible scenario sequences. These techniques have rigorous analytical methods to verify consistency and realtime constraints. To exploit these benefits, identification of all scenarios and scenario sequences is required. This is challenging because of the large number of possible scenarios in modern-day dynamic applications. Manual construction is generally time-consuming and error-prone. In this paper, we address this challenge with an automated approach that extracts a scenario-based analysis model for a class of parallel implementations, which we call Disciplined Dataflow Network (DDN). DDN always guarantees construction of a scenario-based analysis model and enables automating the extraction process. The extraction process identifies all possible scenarios of a DDN and employs state-space enumeration to determine all possible sequences of executions of these scenarios. The approach is demonstrated for the CAL actor language and has been implemented in an openly available CAL compiler. Case studies are presented for the RVC-MPEG video decoder and WLAN 802.11a baseband processing. The case studies show the benefits of automated scenario extraction for efficient design-time analysis of dynamic streaming applications

Automated extraction of scenario sequences from disciplined dataflow networks.

Analysing deadlock-freedom, boundedness and realtime constraints are crucial steps in the design of embedded streaming applications. Dataflow models of computation are often used to analyse such properties at design-time. To that end, scenario-based dataflow techniques isolate the individual operating scenarios of a dynamic application and analyse the executions of the possible scenario sequences. These techniques have rigorous analytical methods to verify consistency and realtime constraints. To exploit these benefits, identification of all scenarios and scenario sequences is required. This is challenging because of the large number of possible scenarios in modern-day dynamic applications. Manual construction is generally time-consuming and error-prone. In this paper, we address this challenge with an automated approach that extracts a scenario-based analysis model for a class of parallel implementations, which we call Disciplined Dataflow Network (DDN). DDN always guarantees construction of a scenario-based analysis model and enables automating the extraction process. The extraction process identifies all possible scenarios of a DDN and employs state-space enumeration to determine all possible sequences of executions of these scenarios. The approach is demonstrated for the CAL actor language and has been implemented in an openly available CAL compiler. Case studies are presented for the RVC-MPEG video decoder and WLAN 802.11a baseband processing. The case studies show the benefits of automated scenario extraction for efficient design-time analysis of dynamic streaming applications

Analyzing synchronous dataflow scenariaos for dynamic software-defined radio applications

Contemporary embedded systems for wireless communications support various radios. A software-defined radio (SDR) is a radio implemented as concurrent software processes that typically run on a multiprocessor system-on-chip (MPSoC). SDRs are real-time streaming applications with throughput requirements. One efficient approach for timing analysis of concurrent real-time applications is the dataflow model of computation (MoC). Nonetheless, the dataflow modeling of SDRs is challenging due to their dynamically changing data processing workload. A dataflow MoC that is not expressive enough to capture this dynamism gives pessimistic throughput results. On the other hand, if it is too expressive and detailed, it may not be analyzable at all. In this paper, we address the challenge of dataflow modeling of SDRs such that their timing behavior can be accurately analyzed to guarantee real-time requirements without unnecessarily over-allocating MPSoC resources. The basis of our modeling approach is splitting the dynamic data processing behavior of a SDR into a group of static modes of operation. Each static mode of operation is then modeled by a Synchronous Dataflow (SDF), which we refer to as scenario. This paper has two main contributions: 1) a scenario-based dataflow model of Long Term Evolution (LTE), which is the latest standard in cellular communication, and 2) investigation of existing throughput analysis techniques of SDF scenarios for our LTE model. Our results show that scenario-based worst-case throughput computation is 2 to 3.4 times more accurate than a state-of-the-art SDF analysis technique. Our investigation also shows that existing timing analysis techniques of SDF scenarios have very low run-time that scales very well with increase in graph size. This makes SDF scenarios suitable in practice for modeling and analyzing SDRs as well as similar dynamic application