Model-Based Design for Embedded Systems- P3 doc

Nicolescu/Model-Based Design for Embedded Systems 67842_C000 Finals Page xx 2009-10-13
xx Contributors
Donald M.Chiarulli
Department of Computer Science
University of Pittsburgh
Pittsburgh, Pennsylvania
MassimilianoD’Angelo
PARADES GEIE
Rome, Italy
Markus Damm
Institute of Computer Technology
Vienna University of Technology
Vienna, Austria
ThaoDang
Verimag Laboratory
Centre National de la Recherche
Scientifique
Grenoble, France
AbhijitDavare
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
Microelectrònica de
Barcelona
Centro
Nacional de Microelectrónica
Universitat Autonòma de Barcelona
Barcelona, Spain
and
Department de Microelectrònica i
Sistemes Electrònics
Universitat Autonòma de Barcelona
Barcelona, Spain
VincentGagne
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
Nicolescu/Model-Based Design for Embedded Systems 67842_C000 Finals Page xxi 2009-10-13
Contributors xxi
Arne Hamann
Institute of Computer and
Network Engineering
Technische Universität
Braunschweig
Braunschweig, Germany
MichaelR.Hansen
Department of Informatics and
Mathematical Modelling
Technical University of Denmark
Lyngby, Denmark

RafikHenia
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
AndréB.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
Mich
elLangevin
STMicroelectronics, Inc.
Ottawa, Ontario, Canada
Kim G. Larsen
Department of Computer Science
Center for Embedded Software
Systems
Aalborg University
Aalborg, Denmark
BrunoLavigueur
STMicroelectronics, Inc.
Ottawa, Ontario, Canada
EdwardA.Lee
University of California at Berkeley
Berkeley, California
Steven P.Levitan
Department of Electrical and
Computer Engineering
University of Pittsburgh

Pittsburgh, Pennsylvania
DavidLo
STMicroelectronics, Inc.
Ottawa, Ontario, Canada
Nicolescu/Model-Based Design for Embedded Systems 67842_C000 Finals Page xxii 2009-10-13
xxii Contributors
Bibiana Lorente-Alvarez
Department de Microelectrònica
Universitat Autonòma de Barcelona
Barcelona, Spain
Jan Madsen
Department of Informatics and
Mathematical Modelling
Technical University of Denmark
Lyngby, Denmark
Jose A. Martinez
Cadence Design Systems, Inc.
San Jose, California
Michel Metzger
STMicroelectronics, Inc.
Ottawa, Ontario, Canada
TrevorMeyerowitz
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
and
PARADES S.c.a.r.l.
Rome,
Italy
Pierre G. Pa
ulin
STMicroelectronics,
Inc.
Ottawa, Ontario, Canada
SimonPerathoner
Computer Engineering and
Networks Laboratory
Swiss Federal Institute of
Technology Zurich
Zurich, Switzerland
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
GerardK.Rauwerda
Recore Systems
Enschede, the Netherlands
Nicolescu/Model-Based Design for Embedded Systems 67842_C000 Finals Page xxiii 2009-10-13
Contributors xxiii
DavidK.Reed
Keynote Systems
San Mateo, California
WolfgangRosenstiel

Forschungszentrum Informatik
Karlsruhe, Germany
Jonas Rox
Institute of Computer and
Network Engineering
Technische Universität
Braunschweig
Braunschweig, Germany
Alberto Sangiovanni-Vincentelli
Department of Electrical
Engineering
and Computer Science
University of California at Berkeley
Berkeley, California
and
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
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
Janos Sztipanovits
Institute for Software Integrated
Systems
Vanderbilt University
Nashville, Tennessee
Ryan Thibodeaux
South West Research Institute
San Antonio, Texas
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
Nicolescu/Model-Based Design for Embedded Systems 67842_C000 Finals Page xxiv 2009-10-13
xxiv Contributors
Yosinori Watanabe
Cadence Design Systems, Inc.
San Jose, California
GuangYang
National Instruments
Corporation
Austin, Texas
HaiyangZheng
University of California at
Berkeley
Berkeley, California
Qi Zhu
Intel Corporation
Santa Clara, California
Nicolescu/Model-Based Design for Embedded Systems 67842_S001 Finals Page 1 2009-10-1
Part I
Real-Time and Performance
Analysis in Heterogeneous
Embedded Systems

Nicolescu/Model-Based Design for Embedded Systems 67842_S001 Finals Page 2 2009-10-1
Nicolescu/Model-Based Design for Embedded Systems 67842_C001 Finals Page 3 2009-10-1
1
Performance Prediction of Distributed
Platforms
Lothar Thiele and Simon Perathoner
CONTENTS
1.1 System-LevelPerformanceAnalysis 3
1.1.1 Distributed Embedded Platforms 4
1.1.2 Role of Performance Analysis in the Design Process 4
1.1.3 Approaches to Performance Analysis 5
1.2 ApplicationScenario 7
1.3 RepresentationintheTimeDomain 9
1.3.1 Arrival and Service Functions 9
1.3.2 Simple and Greedy Components 10
1.3.3 Composition 12
1.4 ModularPerformanceAnalysiswithReal-TimeCalculus 12
1.4.1 Variability Characterization 13
1.4.2 Component Model 14
1.4.3 Component Examples 15
1.4.4 System Performance Model 16
1.4.5 Performance Analysis 17
1.4.6 Compact Representation of VCCs 19
1.5 RTCToolbox 22
1.6 Extensions 23
1.7 ConcludingRemarks 24
Acknowledgments 24
References 25
1.1 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 sys-
tem 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 plat-
forms, and we review different analysis approaches.
3
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4 Model-Based Design for Embedded Systems
1.1.1 Distributed Embedded Platforms
Embedded systems are special-purpose computer systems that are inte-
grated 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 func-
tions that are typically known at the time of design. In general, the knowl-
edge 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 sys-
tems. In addition, the components of a distributed platform are typically het-
erogeneous, 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 subnet-
works, 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 sys-
tems, 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 end-
to-end delays of events, memory demands, and resource usages are required
to support important design decisions. In particular, the designer of a com-
plex 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
Nicolescu/Model-Based Design for Embedded Systems 67842_C001 Finals Page 5 2009-10-1
Performance Prediction of Distributed Platforms 5
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 anal-
ysis 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 imple-
mentation.
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: simulation-
based 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 perfor-
mance 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.