Twan Basten

SDF^3: SDF For Free

SDF^3 is a tool for generating random Synchronous DataFlow Graphs (SDFGs), if desirable with certain guaranteed properties like strongly connectedness. It includes an extensive library of SDFG analysis and transformation algorithms as well as functionality to visualize them. The tool can create SDFG benchmarks that mimic DSP or multimedia applications.

Diagnosing workflow processes using Woflan

Workflow management technology promises a flexible solution for business-process support facilitating the easy creation of new business processes and modification of existing processes. Unfortunately, today’s workflow products have no support for workflow verification. Errors made at design-time are not detected and result in very costly failures at run-time. This paper presents the verification tool Woflan. Woflan analyzes workflow process definitions downloaded from commercial workflow products using state-of-the-art Petri-net-based analysis techniques. This paper describes the functionality of Woflan emphasizing diagnostics to locate the source of a design error. Woflan is evaluated via two case studies, one involving 20 groups of students designing a complex workflow process and one involving an industrial workflow process designed by Staffware Benelux. The results are encouraging and show that Woflan guides the user in finding and correcting errors in the design of workflows. Received 27 September 2000; revised 8 March 2001

Throughput Analysis of Synchronous Data Flow Graphs

Synchronous data flow graphs (SDFGs) are a useful tool for modeling and analyzing embedded data flow applications, both in a single processor and a multiprocessing context or for application mapping on platforms. Throughput analysis of these SDFGs is an important step for verifying throughput requirements of concurrent real-time applications, for instance within design-space exploration activities. Analysis of SDFGs can be hard, since the worst-case complexity of analysis algorithms is often high. This is also true for throughput analysis. In particular, many algorithms involve a conversion to another kind of data flow graph, the size of which can be exponentially larger than the size of the original graph. In this paper, we present a method for throughput analysis of SDFGs, based on explicit state-space exploration and we show that the method, despite its worst-case complexity, works well in practice, while existing methods often fail. We demonstrate this by comparing the method with state-of-the-art cycle mean computation algorithms. Moreover, since the state-space exploration method is essentially the same as simulation of the graph, the results of this paper can be easily obtained as a byproduct in existing simulation tools

System-scenario-based design of dynamic embedded systems

In the past decade, real-time embedded systems have become much more complex due to the introduction of a lot of new functionality in one application, and due to running multiple applications concurrently. This increases the dynamic nature of todays applications and systems, and tightens the requirements for their constraints in terms of deadlines and energy consumption. State-of-the-art design methodologies try to cope with these novel issues by identifying several most used cases and dealing with them separately, reducing the newly introduced complexity. This article presents a generic and systematic design-time/run-time methodology for handling the dynamic nature of modern embedded systems, which can be utilized by existing design methodologies to increase their efficiency. It is based on the concept of system scenarios, which group system behaviors that are similar from a multidimensional cost perspective—such as resource requirements, delay, and energy consumption—in such a way that the system can be configured to exploit this cost similarity. At design-time, these scenarios are individually optimized. Mechanisms for predicting the current scenario at run-time, and for switching between scenarios, are also derived. This design trajectory is augmented with a run-time calibration mechanism, which allows the system to learn on-the-fly during its execution, and to adapt itself to the current input stimuli, by extending the scenario set, changing the scenario definitions, and both the prediction and switching mechanisms. To show the generality of our methodology, we show how it has been applied on four very different real-life design problems. In all presented case studies, substantial energy reductions were obtained by exploiting scenarios.

A scenario-aware data flow model for combined long-run average and worst-case performance analysis

Data flow models are used for specifying and analysing signal processing and streaming applications. However, traditional data flow models are either not capable of expressing the dynamic aspects of modern streaming applications or they do not support relevant analysis techniques. The dynamism in modern streaming applications often originates from different modes of operation (scenarios) in which data production and consumption rates and/or execution times may differ. This paper introduces a scenario-aware generalisation of the synchronous data flow model, which uses a stochastic approach to model the order in which scenarios occur. The formally defined operational semantics of a scenario-aware data flow model implies a Markov chain, which can be analysed for both long-run average and worst-case performance metrics using existing exhaustive or simulation-based techniques. The potential of using scenario-aware data flow models for performance analysis of modern streaming applications is illustrated with an MPEG-4 decoder example

Requirements on the execution of Kahn process networks

Kahn process networks (KPNs) are a programming paradigm suitable for streaming-based multimedia and signal-processing applications. We discuss the execution of KPNs, and the criteria for correct scheduling of their realisations. In [12], Parks shows how process networks can be scheduled in bounded memory; the proposed method is used in many implementations of KPNs. However, it does not result in the correct behaviour for all KPNs. We investigate the requirements for a scheduler to guarantee both correct and bounded execution of KPNs and present an improved scheduling strategy that satisfies them.

Task-level timing models for guaranteed performance in multiprocessor networks-on-chip

We consider a dynamic application running on a multiprocessor network-on-chip as a set of independent jobs, each job possibly running on multiple processors. To provide guaranteed quality and performance, the scheduling of jobs, jobs themselves and the hardware must be amenable to timing analysis. For a certain class of applications and multiprocessor architectures, we propose exact timing models that effectively co-model both the computation and communication of a job. The models are based on interprocessor communication (IPC) graphs [4]. Our main contribution is a precise model of network-on-chip communication, including buffer models. We use a JPEG-decoder job as an example to demonstrate that our models can be used in practice to derive upper bounds on the job execution time and to reason about optimal buffer sizes.

Scenario-aware dataflow: Modeling, analysis and implementation of dynamic applications

Embedded multimedia and wireless applications require a model-based design approach in order to satisfy stringent quality and cost constraints. The Model-of-Computation (MoC) should appropriately capture system dynamics, support analysis and synthesis, and allow low-overhead model-driven implementations. This combination poses a significant challenge. The Scenario-Aware DataFlow (SADF) MoC has been introduced to address this challenge. This paper surveys SADF, and compares dataflow MoCs in terms of their ability to capture system dynamics, their support for analysis and synthesis, and their implementation efficiency.

Ambient intelligence: impact on embedded system design

Foreword H. De Man. Omnia Fieri Possent T. Basten, M. Geilen, H. de Groot. Part I: Challenges. Embedded System Design Issues in Ambient Intelligence E. Aarts, R. Rovers. Ambient Intelligence: A Computational Platform Perspective L. Benini, M. Poncino. Ambient Intelligence and the Development of Embedded System Software A. Purhonen, E. Tuulari. Preparation of Heterogeneous Networks for Ambient Intelligence P. van der Stok. The Butt of the Iceberg: Hidden Security Problems of Ubiquitous Systems F. Stajano, J. Crowcroft. Emerging Challenges in Designing Secure Mobile Appliances S. Ravi, A. Raghunathan, J. -J. Quisquater, S. Hattangady. Part II: Developments. The Domestic Robot - A Friendly Cognitive System Takes Care of your Home F. Pirri, I. Mentuccia, S. Storri. QoS-based Resource Management for Ambient Intelligence C.M. Otero Perez, L. Steffens, P. van der Stok, S. van Loo, A. Alonso, J.E. Ruiz, R.J. Bril, M. Garcia Valls. Terminal QoS: Advanced Resource Management for Cost-Effective Multimedia Appliances in Dynamic Contexts J. Bormans, N. Pham Ngoc, G. Deconinck, G. Lafruit. Scalability and Error Protection - Means for Error-Resilient, Adaptable Image Transmission in Heterogeneous Environments A. Chirila-Rus, G. Lafruit, B. Masschelein. Metaprogramming Techniques for Designing Embedded Components for Ambient Intelligence V. Stuikys, R. Damasevicius. Application-Domain-Driven System Design for Pervasive Video Processing Z. Chamski, M. Duranton, A. Cohen, C. Eisenbeis, P Feautrier, D. Genius. Collaborative Algorithms for Communication in Wireless Sensor Networks T. Nieberg, S. Dulman, P. Havinga, L. van Hoesel, J. Wu. Energy-Efficient Communication for High Density Networks R. Min, A.Chandrakasan. Application Re-mapping for Fault-Tolerance in Ambient Intelligent Systems P. Stanley-Marbell, N.H. Zamora, D. Marculescu, R. Marculescu. Contributing Authors.

Reactive process networks

Data flow process networks are a good model of computation for streaming multimedia applications incorporating audio, video and/or graphics streams. Process networks are concurrent processes communicating streams of data through FIFO channels. They can be executed efficiently and determinately on multiprocessor platforms. However, such stream processing applications are becoming more dynamic, often requiring run-time reconfigurations. Moreover, stream processing is not always an application on its own, but may be a component of a larger application. This application, e.g. a game application, may be control oriented and event driven; events may interact with the streaming component and (re)configure it.In order to capture the interaction between reactive and streaming components as well as reconfiguration in dynamic stream processing, we introduce in this paper a formal, operational and compositional semantics of so-called reactive process networks. This operational semantics can serve as the basis for programming models that allow the programming of streaming components interacting with reactive system components and their reconfigurations. It also supports the construction of analysis and synthesis tools for dynamic streaming multimedia applications. It allows the integration of reactive behaviour in process networks as general as Kahn process networks, but it is also suitable for more restricted and efficient classes of process networks.