Chapter 4 DATAFLOW ANALYSIS FOR REAL-TIME EMBEDDED MULTIPROCESSOR SYSTEM DESIGN

Keywords: Dataflow analysis techniques are key to reduce the number of design iterations and shorten the design time of real-time embedded network based multiprocessor systems that process data streams. With these analysis techniques the worstcase end-to-end temporal behavior of hard real-time applications can be derived from a dataflow model in which computation, communication and arbitration is modeled. For soft real-time applications these static dataflow analysis techniques are combined with simulation of the dataflow model to test statistical assertions about their temporal behavior. The simulation results in combination with properties of the dataflow model are used to derive the sensitivity of design parameters and to estimate parameters like the capacity of data buffers. real-time, dataflow analysis, multiprocessor system, predictable design, systemon-chip 1

Analyzing Concurrency in Computational Networks (Extended Abstract

We present a concurrency model that allows reasoning about concurrency in executable specifications. The model mainly focuses on data-flow and streaming applications and at task-level concurrency. The aim of the model is to provide insight in concurrency bottlenecks in an application and to provide support for performing implementationindependen

T.: Analyzing concurrency in streaming applications

We present a concurrency model that allows reasoning about concurrency in executable specifications of streaming applications. It provides measures for five different concurrency properties. The aim of the model is to provide insight in concurrency bottlenecks in an application and to provide global direction when performing implementation-independent concurrency optimization. The model focuses on task-level concurrency. A concurrency optimization method and a prototype implementation of a supporting analysis tool have been developed. We use the model and tool to optimize the concurrency in a number of multimedia applications. The results show that the concurrency model allows targetarchitecture-independent concurrency optimization.

Exploring trade-offs in buffer requirements and throughput constraints for synchronous dataflow graphs

Multimedia applications usually have throughput constraints. An implementation must meet these constraints, while it minimizes resource usage and energy consumption. The compute intensive kernels of these applications are often specified as Synchronous Dataflow Graphs. Communication between nodes in these graphs requires storage space which influences throughput. We present an exact method to determine the minimal storage space needed to execute a graph under a given throughput constraint. We also show how this method can be used to chart the Pareto space of throughput and storage trade-offs. The feasibility of the approach is demonstrated with a number of examples.

Minimising buffer requirements of synchronous dataflow graphs with model checking

Signal processing and multimedia applications are often implemented on resource constrained embedded systems. It is therefore important to find implementations that use as little resources as possible. These applications are frequently specified as synchronous dataflow graphs. Communication between actors of these graphs requires storage capacity. In this paper, we present an exact method to determine the minimum storage capacity required to execute the graph using model-checking techniques. This can be done for different measures of storage capacity. The problem is known to be NP-complete and because of this, existing buffer minimisation techniques are heuristics and hence not exact. Modern model-checking tools are quite efficient and they have been successfully applied to scheduling-related problems. We study the feasibility of this approach with examples

ABSTRACT

Multimedia applications usually have throughput constraints. An implementation must meet these constraints, while it minimizes resource usage and energy consumption. The compute intensive kernels of these applications are often specified as Synchronous Dataflow Graphs. Communication between nodes in these graphs requires storage space which influences throughput. We present exact techniques to chart the Pareto space of throughput and storage tradeoffs, which can be used to determine the minimal storage space needed to execute a graph under a given throughput constraint. The feasibility of the approach is demonstrated with a number of examples. 1