Model-Based Design for Embedded Systems Computational Analysis, Synthesis, and Design of Dynamic Models Series Series Editor Pieter J Mosterman The MathWorks Natick, Massachusetts Discrete-Event Modeling and Simulation: A Practitioner's Approach, Gabriel A Wainer Discrete-Event Modeling and Simulation: Theory and Applications, edited by Gabriel A Wainer and Pieter J Mosterman Model-Based Design for Embedded Systems, edited by Gabriela Nicolescu and Pieter J Mosterman Model-Based Testing for Embedded Systems, edited by Justyna Zander, Ina Schieferdecker, and Pieter J Mosterman Multi-Agent Systems: Simulation & Applications, edited by Adelinde M Uhrmacher and Danny Weyns Model-Based Design for Embedded Systems Gabriela Nicolescu Pieter J Mosterman Boca Raton London New York CRC Press is an imprint of the Taylor & Francis Group, an informa business MATLAB® and Simulink® are trademarks of The MathWorks, Inc and are used with permission The MathWorks does not warrant the accuracy of the 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have been granted a photocopy license by the CCC, a separate system of payment has been arranged Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe Library of Congress Cataloging-in-Publication Data Model-based design for embedded systems / Gabriela Nicolescu, Pieter J Mosterman p cm (Computational analysis, synthesis, and design of dynamic models series) Includes bibliographical references and index ISBN 978-1-4200-6784-2 (hardcover : alk paper) Embedded computer systems Design and construction I Nicolescu, G (Gabriela) II Mosterman, Pieter J III Title IV Series TK7895.E42M62 2010 004.16 dc22 Visit the Taylor & Francis Web site at http://www.taylorandfrancis.com and the CRC Press Web site at http://www.crcpress.com 2009036996 Contents Preface ix Introduction xi Contributors xix Part I Real-Time and Performance Analysis in Heterogeneous Embedded Systems Performance Prediction of Distributed Platforms Lothar Thiele and Simon Perathoner SystemC-Based Performance Analysis of Embedded Systems Jürgen Schnerr, Oliver Bringmann, Matthias Krause, Alexander Viehl, and Wolfgang Rosentiel Formal Performance Analysis for Real-Time Heterogeneous Embedded Systems Simon Schliecker, Jonas Rox, Rafik Henia, Razvan Racu, Arne Hamann, and Rolf Ernst Model-Based Framework for Schedulability Analysis Using UPPAAL 4.1 Alexandre David, Jacob Illum, Kim G Larsen, and Arne Skou 27 57 93 Modeling and Analysis Framework for Embedded Systems 121 Jan Madsen, Michael R Hansen, and Aske W Brekling TrueTime: Simulation Tool for Performance Analysis of Real-Time Embedded Systems 145 Anton Cervin and Karl-Erik Årzén v vi Contents Part II Design Tools and Methodology for Multiprocessor System-on-Chip MPSoC Platform Mapping Tools for Data-Dominated Applications 179 Pierre G Paulin, Olivier Benny, Michel Langevin, Youcef Bouchebaba, Chuck Pilkington, Bruno Lavigueur, David Lo, Vincent Gagne, and Michel Metzger Retargetable, Embedded Software Design Methodology for Multiprocessor-Embedded Systems 207 Soonhoi Ha Programming Models for MPSoC 231 Katalin Popovici and Ahmed Jerraya 10 Platform-Based Design and Frameworks: METROPOLIS and METRO II 259 Felice Balarin, Massimiliano D’Angelo, Abhijit Davare, Douglas Densmore, Trevor Meyerowitz, Roberto Passerone, Alessandro Pinto, Alberto Sangiovanni-Vincentelli, Alena Simalatsar, Yosinori Watanabe, Guang Yang, and Qi Zhu 11 Reconfigurable Multicore Architectures for Streaming Applications 323 Gerard J M Smit, André B J Kokkeler, Gerard K Rauwerda, and Jan W M Jacobs 12 FPGA Platforms for Embedded Systems 351 Stephen Neuendorffer Part III Design Tools and Methodology for Multidomain Embedded Systems 13 Modeling, Verification, and Testing Using Timed and Hybrid Automata 383 Stavros Tripakis and Thao Dang 14 Semantics of Domain-Specific Modeling Languages 437 Ethan Jackson, Ryan Thibodeaux, Joseph Porter, and Janos Sztipanovits 15 Multi-Viewpoint State Machines for Rich Component Models 487 Albert Benveniste, Bent Caillaud, and Roberto Passerone Contents vii 16 Generic Methodology for the Design of Continuous/Discrete Co-Simulation Tools 519 Luiza Gheorghe, Gabriela Nicolescu, and Hanifa Boucheneb 17 Modeling and Simulation of Mixed Continuous and Discrete Systems 559 Edward A Lee and Haiyang Zheng 18 Design Refinement of Embedded Mixed-Signal Systems 585 Jan Haase, Markus Damm, and Christoph Grimm 19 Platform for Model-Based Design of Integrated Multi-Technology Systems 603 Ian O’Connor 20 CAD Tools for Multi-Domain Systems on Chips 643 Steven P Levitan, Donald M Chiarulli, Timothy P Kurzweg, Jose A Martinez, Samuel J Dickerson, Michael M Bails, David K Reed, and Jason M Boles 21 Smart Sensors Modeling Using VHDL-AMS for Microinstrument Implementation with a Distributed Architecture 697 Carles Ferrer, Laura Barrachina-Saralegui, and Bibiana Lorente-Alvarez Index 719 Preface The unparalleled flexibility of computation has been a key driver and feature bonanza in the development of a wide range of products across a broad and diverse spectrum of applications such as in the automotive aerospace, health care, consumer electronics, etc Consequently, the embedded microprocessors that implement computational functionality have become a part of almost every facet of our world, thereby significantly improving the quality of our lives The versatility of computational features invites and endorses a degree of imagination and creativity in design that has unlocked an almost insatiable demand for consistently increasing both the complexity of embedded systems and the performance of embedded computations The quest to rise to these demands has resulted in computing architectures of a heterogeneous nature These architectures often integrate several types of processors, analog and digital electronic components, as well as mechanical and optical components, all on a single chip To efficiently design for such heterogeneity and to maximally exploit its capabilities have become one of the most prominent challenges that we are now faced with as a design automation community Model-Based Design is emerging as a solution to bridge the gap between computational capabilities that are available but that we are yet unable to exploit Using a computational approach in the design itself allows raising the level of abstraction of the system specification at which novel and differentiating functionalities are captured Automation can then assist in refining this specification to an implementation For this to be successful, performance studies of potential implementations at a high level of abstraction are essential, combined with the necessity of traceability and parameterization throughout the refinement process This book provides a compilation of the work of internationally renowned authors on Model-Based Design Each chapter contributes supreme results that have helped establish Model-Based Design and that continue to expand its barriers The respective authors excel in their expertise on the automation of design refinement and how to relate properties throughout this refinement while enabling analytic and synthetic qualities We are delighted and honored by their participation in the effort that led to this book, and we sincerely hope that the readers will find the indulgence of intellectual achievement as enjoyable and stimulating as we In closing, we would like to express our genuine appreciation and gratitude for all the time and effort that each of the authors has put in Our ix Introduction xv Chapter presents a definition of the programming models that abstract hardware/software interfaces in the case of heterogeneous MPSoCs Then, a programming environment is proposed that identifies several programming models at different MPSoC abstraction levels The proposed approach combines the Simulink environment for high-level programming and the SystemC design language for low-level programming The proposed methodology is applied to a heterogeneous multiprocessor platform, to explore the communication architecture and to generate efficient executable code of the software stack for an H.264 video encoder application Chapter 10 discusses design principles and how a unified methodology together with a supporting software framework can be developed to improve the level of efficiency of the embedded electronics industry This chapter first presents the design challenges for future systems and a manifesto espousing the benefits of a unified methodology Then a methodology, a platform-based design, is summarized The chapter proceeds to present Metropolis, a software framework supporting the methodology, and Metro II, a second-generation framework tailored to industrial test cases It concludes with two test cases in diverse domains: semiconductor chips (a universal mobile telecommunication system multichip design) and energyefficient buildings (an indoor air quality control system) Chapter 11 presents reconfigurable heterogeneous and homogeneous multicore SoC platforms for streaming digital signal–processing (DSP) applications Typical examples of streaming DSP applications are wireless baseband processing, multimedia processing, medical image processing, sensor processing (e.g., for remote surveillance cameras), and phased-array radars This chapter first introduces streaming applications and multicore architectures, presents key design criteria for streaming applications, and concludes with a multidimensional classification of architectures for streaming applications For each category, one or more sample architectures are presented Chapter 12 describes the use of partial reconfiguration capabilities of some field programmable gate array (FPGAs) to provide a platform that is similar to existing general-purpose FPGAs Partial reconfiguration involves the reconfiguration of part of an FPGA (a reconfigurable region) while another part of the FPGA (a static region) remains active and operating This chapter illustrates this approach by presenting a case study on the design of a software-defined radio platform Part III Design Tools and Methodology for Multidomain Embedded Systems Part III covers Model-Based Design for multidomain systems Continuoustime and discrete-event models are at the core of Model-Based Design for these systems This part of the book is composed of nine chapters and addresses the following challenges: validating and testing traditional formal models used for blending the continuous and discrete worlds, defining semantics for combining models specific to different domains, defining and xvi Introduction exploiting new languages that embrace the heterogeneity of domains, unambiguous specification of semantics for domain-specific modeling languages (DSMLs), and developing new methodologies for Model-Based Design for that are able to take into account the heterogeneity in multidomain systems Model-Based Design for illustrative heterogeneous systems such as optoelectromechanical and mixed-signal systems are discussed in detail Chapter 13 provides a comprehensive overview of modeling with timed and hybrid automata These types of automata have been introduced in order to blend the discrete world of computers with the continuous physical world This chapter presents the basics of timed and hybrid automata models and methods for exhaustive or partial verification, as well as testing for these models Chapter 14 captures the fundamental problems, methods, and techniques for specifying the semantics of DSMLs The effective application of DSMLs for an embedded design requires developers to have an unambiguous specification of the semantics of modeling languages This chapter explores two key aspects of this problem: the specifications of structural and behavioral semantics Chapter 15 emphasizes combining different modeling perspectives and provides a simple and elegant notion of parallel composition This chapter first reviews the concepts of “component” and “contract” from a semantic point of view Then, the extended state machine model is described The syntax and the expressive power used for expressions in the transitions of the state-based model are reviewed, followed by the specialization of the model into different categories to support alternative perspectives Chapter 16 presents an approach to solve the problem of combining continuous-time and discrete-event execution models This chapter focuses on the analysis of the two execution models and on the definition of models for simulation interfaces required for combining these models in a global continuous/discrete execution model It proposes a generic methodology, independent of the simulation language, for the design of continuous/ discrete cosimulation tools Chapter 17 provides an operational semantics that supports a combination of synchronous/reactive (SR) systems, discrete-event (DE) systems, and continuous-time (CT) dynamics This chapter outlines a corresponding denotational semantics Dialects of DE and CT are developed that generalize SR but provide complementary modeling and design capabilities Chapter 18 provides an overview of the analog, mixed signal (AMS) extensions for SystemC With these extensions, SystemC becomes amenable to modeling HW/SW systems and—at the function and architecture levels— analog and mixed-signal subsystems The intended uses include executable specification, architecture exploration, virtual prototyping, and integration validation This chapter describes a methodology that efficiently exploits the AMS extensions together with newly introduced converter channels The methodology is illustrated by applying it to a software-defined radio system Introduction xvii Chapter 19 presents several aspects of heterogeneous design methods in the context of increasing diversification of integration technologies This chapter first provides the rationale and analysis of the multitechnology need in terms of technological evolution and highlights the need for advances in this domain It then presents RuneII , a platform that addresses some of these needs Finally, it illustrates the direct application of the proposed approach for optical link synthesis and technology performance characterization by analyzing optical link performance for two sets of photonic component parameters and three CMOS technology generations Chapter 20 concentrates on multidomain modeling and multirate simulation tools that are required to support mixed-technology system-level design This chapter proposes the Chatoyant environment for simulating and analyzing optical microelectromechanical systems (MEMSs) By supporting a variety of multidomain components and signal modeling techniques at multiple levels of abstraction, Chatoyant has the ability to perform and analyze mixed-signal trade-offs, which makes it invaluable to multitechnology system designers Chapter 21 underscores the importance of the role of behavioral modeling in the design of multidomain systems This chapter presents a case study where mixed-signal hardware description languages are used to specify and simulate systems composed of elements of a different nature A VHDLAMS-based approach is applied for the behavioral modeling of MEMS-based microinstrumentation References [Gan03] J Gannsle and M Barr, Embedded Systems Dictionary, CMP Books, San Francisco, CA, 2003 [ITR07] International Technology Roadmap for Semiconductors, ITRS 2007 Rapport [Jer04] A Jerraya and W Wolf, Multiprocessors Systems-on-Chip, Morgan Kaufmann, San Francisco, CA, 2004 [TUM06] R Tummala, Moore’s law meets its match, IEEE Spectrum, 43(6), 44–49, June 2006 Issue [ZHA06] G Q Zhang, M Graef, and F van Roosmalen, Strategic research agenda of “More than Moore,” in Proceedings of EuroSime 2006, Como, Italy, pp 1–6, April 24–26, 2006 Contributors Karl-Erik Årzén Department of Automatic Control Lund Institute of Technology Lund University Lund, Sweden Michael M Bails FedEx Ground Pittsburgh, Pennsylvania Felice Balarin Cadence Berkeley Labs Berkeley, California Laura Barrachina-Saralegui Institut de Microelectrònica de Barcelona Centre Nacional de Microelectrònica Barcelona, Spain Olivier Benny STMicroelectronics, Inc Ottawa, Ontario, Canada Albert Benveniste Institut de Recherche en Informatique et Systèmes Aléatoires Institut National de Recherche en Informatique et en Automatique Rennes, France Jason M Boles Department of Computational Biology University of Pittsburgh Pittsburgh, Pennsylvania Youcef Bouchebaba STMicroelectronics, Inc Ottawa, Ontario, Canada Hanifa Boucheneb Department of Computer and Software Engineering Ecole Polytechnique de Montreal Montreal, Quebec, Canada Aske W Brekling Department of Informatics and Mathematical Modelling Technical University of Denmark Lyngby, Denmark Oliver Bringmann Forschungszentrum Informatik Karlsruhe, Germany Bent Caillaud Institut de Recherche en Informatique et Systèmes Aléatoires Institut National de Recherche en Informatique et en Automatique Rennes, France Anton Cervin Department of Automatic Control Lund Institute of Technology Lund University Lund, Sweden xix xx Contributors Donald M Chiarulli Department of Computer Science University of Pittsburgh Pittsburgh, Pennsylvania Massimiliano D’Angelo PARADES GEIE Rome, Italy Markus Damm Institute of Computer Technology Vienna University of Technology Vienna, Austria Thao Dang Verimag Laboratory Centre National de la Recherche Scientifique Grenoble, France Abhijit Davare Intel Corporation Santa Clara, California Alexandre David Department of Computer Science Center for Embedded Software Systems Aalborg University Aalborg, Denmark Douglas Densmore Department of Electrical Engineering and Computer Science University of California at Berkeley Berkeley, California Samuel J Dickerson Department of Electrical and Computer Engineering University of Pittsburgh Pittsburgh, Pennsylvania Rolf Ernst Institute of Computer and Network Engineering Technische Universität Braunschweig Braunschweig, Germany Carles Ferrer Instituto de Microelectrịnica de Barcelona Centro Nacional de Microelectrónica Universitat Autonịma de Barcelona Barcelona, Spain and Department de Microelectrònica i Sistemes Electrònics Universitat Autonòma de Barcelona Barcelona, Spain Vincent Gagne STMicroelectronics, Inc Ottawa, Ontario, Canada Luiza Gheorghe Department of Computer and Software Engineering Ecole polytechnique de Montreal Montreal, Quebec, Canada Christoph Grimm Institute of Computer Technology Vienna University of Technology Vienna, Austria Soonhoi Ha School of Computer Science and Engineering Seoul National University Seoul, Republic of Korea Jan Haase Institute of Computer Technology Vienna University of Technology Vienna, Austria Contributors Arne Hamann Institute of Computer and Network Engineering Technische Universität Braunschweig Braunschweig, Germany Michael R Hansen Department of Informatics and Mathematical Modelling Technical University of Denmark Lyngby, Denmark Rafik Henia Institute of Computer and Network Engineering Technische Universität Braunschweig Braunschweig, Germany Jacob Illum Department of Computer Science Center for Embedded Software Systems Aalborg University Aalborg, Denmark Ethan Jackson Microsoft Research Redmond, Washington Jan W M Jacobs OCE Technologies Venlo, the Netherlands Ahmed Jerraya Atomic Energy Commission Laboratory of the Electronics and Information Technology MINATEC Grenoble, France xxi André B J Kokkeler Department of Electrical Engineering, Mathematics and Computer Science University of Twente Enschede, the Netherlands Matthias Krause Forschungszentrum Informatik Karlsruhe, Germany Timothy P Kurzweg Department of Electrical and Computer Engineering Drexel University Philadelphia, Pennsylvania Michel Langevin STMicroelectronics, Inc Ottawa, Ontario, Canada Kim G Larsen Department of Computer Science Center for Embedded Software Systems Aalborg University Aalborg, Denmark Bruno Lavigueur STMicroelectronics, Inc Ottawa, Ontario, Canada Edward A Lee University of California at Berkeley Berkeley, California Steven P Levitan Department of Electrical and Computer Engineering University of Pittsburgh Pittsburgh, Pennsylvania David Lo STMicroelectronics, Inc Ottawa, Ontario, Canada xxii Contributors Bibiana Lorente-Alvarez Department de Microelectrònica Universitat Autonòma de Barcelona Barcelona, Spain Pierre G Paulin STMicroelectronics, Inc Ottawa, Ontario, Canada Jan Madsen Department of Informatics and Mathematical Modelling Technical University of Denmark Lyngby, Denmark Simon Perathoner Computer Engineering and Networks Laboratory Swiss Federal Institute of Technology Zurich Zurich, Switzerland Jose A Martinez Cadence Design Systems, Inc San Jose, California Michel Metzger STMicroelectronics, Inc Ottawa, Ontario, Canada Trevor Meyerowitz Sun Microsystems Menlo Park, California Stephen Neuendorffer Xilinx Research Labs San Jose, California Gabriela Nicolescu Department of Computer and Software Engineering Ecole Polytechnique de Montreal Montreal, Quebec, Canada Ian O’Connor Lyon Institute of Nanotechnology Ecole Centrale de Lyon University of Lyon Ecully, France Roberto Passerone Dipartimento di Ingegneia e Scienza dell’ Informazione University of Trento Trento, Italy Chuck Pilkington STMicroelectronics, Inc Ottawa, Ontario, Canada Alessandro Pinto United Technology Research Center Berkeley, California Katalin Popovici TIMA Laboratory Grenoble, France and The MathWorks, Inc Natick, Massachusetts Joseph Porter Institute for Software Integrated Systems Vanderbilt University Nashville, Tennessee Razvan Racu Institute of Computer and Network Engineering Technische Universität Braunschweig Braunschweig, Germany and PARADES S.c.a.r.l Rome, Italy Gerard K Rauwerda Recore Systems Enschede, the Netherlands Contributors David K Reed Keynote Systems San Mateo, California Wolfgang Rosenstiel Forschungszentrum Informatik Karlsruhe, Germany Jonas Rox Institute of Computer and Network Engineering Technische Universität Braunschweig Braunschweig, Germany Arne Skou Department of Computer Science Center for Embedded Software Systems Aalborg University Aalborg, Denmark Gerard J M Smit Department of Electrical Engineering, Mathematics & Computer Science University of Twente Enschede, the Netherlands Alberto Sangiovanni-Vincentelli Department of Electrical Engineering and Computer Science University of California at Berkeley Berkeley, California Janos Sztipanovits Institute for Software Integrated Systems Vanderbilt University Nashville, Tennessee and Ryan Thibodeaux South West Research Institute San Antonio, Texas Advanced Laboratory on Embedded Systems Roma, Italy Simon Schliecker Institute of Computer and Network Engineering Technische Universität Braunschweig Braunschweig, Germany Jürgen Schnerr Forschungszentrum Informatik Karlsruhe, Germany Alena Simalatsar Dipartimento di Ingegneria e Scienza dell’ Informazione University of Trento Trento, Italy xxiii Lothar Thiele Computer Engineering and Networks Laboratory Swiss Federal Institute of Technology Zurich Zurich, Switzerland Stavros Tripakis Verimag Laboratory Centre National de la Recherche Scientifique Grenoble, France Alexander Viehl Forschungszentrum Informatik Karlsruhe, Germany xxiv Yosinori Watanabe Cadence Design Systems, Inc San Jose, California Guang Yang National Instruments Corporation Austin, Texas Contributors Haiyang Zheng University of California at Berkeley Berkeley, California Qi Zhu Intel Corporation Santa Clara, California Part I Real-Time and Performance Analysis in Heterogeneous Embedded Systems Performance Prediction of Distributed Platforms Lothar Thiele and Simon Perathoner CONTENTS 1.1 System-Level Performance Analysis 1.1.1 Distributed Embedded Platforms 1.1.2 Role of Performance Analysis in the Design Process 1.1.3 Approaches to Performance Analysis 1.2 Application Scenario 1.3 Representation in the Time Domain 1.3.1 Arrival and Service Functions 1.3.2 Simple and Greedy Components 1.3.3 Composition 1.4 Modular Performance Analysis with Real-Time Calculus 1.4.1 Variability Characterization 1.4.2 Component Model 1.4.3 Component Examples 1.4.4 System Performance Model 1.4.5 Performance Analysis 1.4.6 Compact Representation of VCCs 1.5 RTC Toolbox 1.6 Extensions 1.7 Concluding Remarks Acknowledgments References 1.1 4 9 10 12 12 13 14 15 16 17 19 22 23 24 24 25 System-Level Performance Analysis One of the major challenges in the design process of distributed embedded systems is to accurately predict performance characteristics of the final system implementation in early design stages This analysis is generally referred to as the system-level performance analysis In this section, we introduce the relevant properties of distributed embedded systems, we describe the role of the system-level performance analysis in the design process of such platforms, and we review different analysis approaches Model-Based Design for Embedded Systems 1.1.1 Distributed Embedded Platforms Embedded systems are special-purpose computer systems that are integrated into products such as cars, telecommunication devices, consumer electronics, and medical equipment In contrast to general-purpose computer systems, embedded systems are designed to perform few dedicated functions that are typically known at the time of design In general, the knowledge about the specific application domain and the behavior of the system is exploited to develop customized and optimized system designs Embedded systems must be efficient in terms of power consumption, size, and cost In addition, they usually have to be fully predictable and highly dependable, as a malfunction or a breakdown of the device they may control is in general not acceptable The embedding into large products and the constraints imposed by the environment often require distributed implementations of embedded systems In addition, the components of a distributed platform are typically heterogeneous, as they perform different functionalities and are adapted to the particular local environment Also the interconnection networks are often not homogeneous, but may be composed of several interconnected subnetworks, each one with its own topology and communication protocol The individual processing nodes are typically not synchronized They operate in parallel and communicate via message passing They make autonomous decisions concerning resource sharing and scheduling of tasks Therefore, it is particularly difficult to maintain a global-state information of the system Many embedded systems are reactive systems that are in a continuous interaction with their environment through sensors and actuators Thus, they often have to execute at a pace determined by their environment, which means that they have to meet real-time constraints For these kinds of systems, the predictability in terms of execution time is as important as the result of the processing itself: a correct result arriving later (or even earlier) than expected is wrong Based on the characteristics described above, it becomes apparent that heterogeneous and distributed embedded real-time systems are inherently difficult to design and to analyze, particularly, as not only the availability and the correctness of the processed results, but also the timeliness of the computations are of major concern 1.1.2 Role of Performance Analysis in the Design Process Reliable predictions of performance characteristics of a system such as endto-end delays of events, memory demands, and resource usages are required to support important design decisions In particular, the designer of a complex embedded system typically has to cope with a large design space that is given by the numerous alternatives for partitioning, allocation, and binding in the system design Thus, he or she often needs to evaluate the performance of many design options in order to optimize the trade-offs between several Performance Prediction of Distributed Platforms Application Architecture Allocation, binding, scheduling Performance analysis Design space exploration FIGURE 1.1 Performance analysis in the design space exploration cycle design objectives In such a design space exploration, the performance analysis plays a crucial role, as can be seen in Figure 1.1 Methods and tools for expedient and reliable performance analyses of system specifications at a high abstraction level are not only needed to drive the design space exploration but also for verification purposes In particular, they permit to guarantee the functionality of a system in terms of real-time constraints before much time and resources are invested for its actual implementation 1.1.3 Approaches to Performance Analysis The need for accurate performance predictions in early design stages has driven research for many years Most of the approaches for performance analysis proposed so far can be broadly divided into two classes: simulationbased methods and analytic techniques There are also stochastic methods for performance analysis; however, we will not discuss them further in this context Simulation-based methods for performance estimation are widely used in industry There are several commercial tools that support cycle-accurate cosimulation of complete HW/SW systems Besides commercial tool suites, there also exist free simulation frameworks that can be applied for performance estimation, such as SystemC [9] The main advantage of simulation-based performance estimation approaches is their large and customizable modeling scope, which permits to take into account various complex interactions and correlations in a system ... system-level performance analysis in the design process of such platforms, and we review different analysis approaches Model-Based Design for Embedded Systems 1.1.1 Distributed Embedded Platforms Embedded. .. a case study on the design of a software-defined radio platform Part III Design Tools and Methodology for Multidomain Embedded Systems Part III covers Model-Based Design for multidomain systems... (DSMLs), and developing new methodologies for Model-Based Design for that are able to take into account the heterogeneity in multidomain systems Model-Based Design for illustrative heterogeneous systems