The Artemis workbench for system-level performance evaluation of embedded systems Pimente

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UvA-DARE (Digital Academic Repository)
The Artemis workbench for system-level performance evaluation of embedded
systems
Pimentel, A.D.
DOI
10.1504/IJES.2008.020299
Publication date
2008
Published in
International Journal of Embedded Systems
Link to publication
Citation for published version (APA):
Pimentel, A. D. (2008). The Artemis workbench for system-level performance evaluation of
embedded systems. International Journal of Embedded Systems, 3(3), 181-196.
https://doi.org/10.1504/IJES.2008.020299
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Download date:07 Mar 2023
The Artemis Workbench for System-level
Performance Evaluation of Embedded Systems
Andy D. Pimentel, Member, IEEE Computer Society
Abstract(cid:151)In this article, we present an overview of the and (cid:3)exibility. This has led to the notion of a new design
Artemis workbench, which provides modelling and simulation methodology, namely system-level design. Below, we brie(cid:3)y
methods and tools for ef(cid:2)cient performance evaluation and describe three important ingredients of system-level design
exploration of heterogeneous embedded multimedia systems.
approaches.
Morespeci(cid:2)cally,wedescribetheArtemissystem-levelmodelling
methodology, including its support for gradual re(cid:2)nement of 1) Platform architectures
architectureperformancemodelsaswellasforcalibrationofthe
Platform-based design [46], [52] has become a popular
system-level models. We show that this methodology allows for
designmethodasitstressesthere-useofIP(Intellectual
architectural exploration at different levels of abstraction while
maintaining high-level and architecture independentapplication Property)blocks.Inthisdesignapproach,a singlehard-
speci(cid:2)cations. Moreover, we illustrate these modelling aspects wareplatformis usedas a(cid:147)hardwaredenominator(cid:148)that
using a case study with a Motion-JPEG application. issharedacrossmultipleapplicationsin agivendomain
Index Terms(cid:151)System-level modelling and simulation, model and is accompanied by a range of methods and tools
re(cid:2)nement, performance evaluation, design space exploration, for design and development. This increases production
model calibration. volumeandreducescostcomparedtocustomisingachip
for every application.
I. INTRODUCTION 2) Separation of concerns
To even further improve the potentials for re-use of
DESIGNERSofmodernembeddedsystemsarefacedwith
IP and to allow for effective exploration of alternative
a number of emerging challenges. Because embedded
design solutions, it is widely recognised that the (cid:147)sep-
systems are mostly targeted for mass production and often
aration of concerns(cid:148) is a crucial component in system-
run on batteries, they should be cheap to realise and power
level design [22]. Two common types of separation in
ef(cid:2)cient. In addition, these systems increasingly need to
the design process are: i) separating computation from
support multiple applications and standards for which they
communicationby connectingIP processingcoresvia a
should provide high, and sometimes real-time, performance.
standard(message-passing)networkinterface[5]andii)
For example, digital televisions or mobile devices have to
separating application (what is the system supposed to
support different standards for communication and coding
do) from architecture (how it does it) [3], [23].
of digital contents. On top of this, modern embedded sys-
3) High-levelmodellingandsimulationearly inthedesign
tems should be (cid:3)exible so that they can easily be extended
Insystem-leveldesign,designersalreadystartwithmod-
to support future applications and standards. Such (cid:3)exible
ellingandsimulating(possible)systemcomponentsand
support for multiple applications calls for a high degree
their interactions in the early design stages [43]. More
of programmability. However, performance requirements and
speci(cid:2)cally, system-level models typically represent ap-
constraintsoncostandpowerconsumptionrequiresubstantial
plication behaviour,architecture characteristics, and the
parts of these systems to be implemented in dedicated hard-
relation (e.g., mapping, hardware-software partitioning)
ware blocks. As a result, modern embedded systems often
between application(s) and architecture. These models
have a heterogeneoussystem architecture, i.e., they consist of
do so at a high level of abstraction, thereby minimising
componentsrangingfromfullyprogrammableprocessorcores
the modelling effort and optimising simulation speed
to fully dedicated hardware components for the time-critical
thatis neededfortargetingtheearlydesignstages. This
application tasks. Increasingly, such heterogeneous systems
high-level modelling allows for early veri(cid:2)cation of a
are integrated on a single chip. This yields heterogeneous
design and can provide estimations on the performance
multi-processor Systems-on-Chip (SoCs) that exploit task-
(e.g., [3], [43]), power consumption (e.g., [6]) or cost
level parallelism in applications.
[13] of the design.
The heterogeneity of modern embedded systems and the
varyingdemandsoftheirtargetapplicationsgreatlycomplicate Design space exploration plays a crucial role in system-
the system design. It is widely agreed upon that traditional leveldesignofembeddedsystem(platform)architectures.Due
design methods fall short for the design of these systems to the systems’ complexity, it is imperative to have good
as such methods cannot deal with the systems’ complexity performance evaluation tools for ef(cid:2)ciently exploring a wide
rangeofdesignchoicesduringthe earlydesignstages. Inthis
A.D. Pimentel is with the Dept. of Computer Science of the University
article, we present an overview of the Artemis workbench,
ofAmsterdam, Kruislaan 403, 1098 SJAmsterdam, The Netherlands. He is
project coordinator oftheArtemisproject. Email:[email protected] which provides high-level modelling and simulation methods
andtools for ef(cid:2)cient performanceevaluationandexploration Application domain
of heterogeneous embedded multimedia systems [43]. More
speci(cid:2)cally, we describe the Artemis system-level modelling
Application specification
methodology (cid:150) which deploys the aforementioned principle Architecture template (sequential)
of separation of concerns [22] (cid:150) and particularly focus on
thesupportforgradualre(cid:2)nementofarchitectureperformance
Translator
models as well as on the calibration of system-level models.
Weshowthatthismethodologyallowsforarchitecturalexplo-
rationatdifferentlevelsofabstractionwhilemaintaininghigh- Architecture instance Applica(ctioonnc suprreecnifti)cation
level and architecture independent application speci(cid:2)cations. entire
Furthermore,weillustratethesemodellingaspectsusingacase specification
study with a Motion-JPEG encoder application. System−level modelling and simulation
part of
1) Mapping and Refinement
The remainder of the article is organised as follows. The specification
2) Quantitative performance analysis
next section provides a birds eye view of the Artemis work- by means of co−simulation
− on levels of abstraction
bench, brie(cid:3)y discussing the different tool-sets that are in- − across levels of abstraction
tegrated in the workbench. Section III focuses on Artemis’
system-level modellingand simulationtechniques by describ- Component
low−level calibration
ingitsprototypemodellingandsimulationenvironment,called numbers
Sesame. In Section IV, we explain how Artemis facilitates
Performance numbers
gradual re(cid:2)nement of system-level architecture models by
applying data(cid:3)ow graphs. Section V illustrates the discussed
Fig.1. Theinfrastructure oftheArtemisworkbench.
modelling and re(cid:2)nement techniques using a case study
with a Motion-JPEG encoder application. This section also
demonstrates how our system-level models can be calibrated
will elaborate on all of the above modelling, mapping, and
withlow-levelimplementationinformationusinganautomated
re(cid:2)nement aspects in more detail.
componentcalibrationapproach.Finally,SectionVIdiscusses
Because Artemis operates at a high level of abstraction,
related work, after which Section VII concludes the article.
low-level component performance numbers can be used to
calibrate the system-level architecture models. To this end,
II. THEARTEMISWORKBENCH individualprocesses(i.e.,codesegments)ofaKPNapplication
speci(cid:2)cation are taken apart and implemented as individual
The Artemis workbench consists of a set of methods and
low-level components (that appear in the current high-level
tools conceived and integrated in a framework to allow de-
instance of the platform architecture). This results in per-
signersto modelapplicationsandSoC-based(multiprocessor)
formance numbers (cid:150) as well as in estimations on cost and
architectures at a high level of abstraction,to map the former
power consumption (cid:150) for the low-level components that the
onto the latter, and to estimate performancenumbers through
system-level modelling framework needs to provide accu-
co-simulationofapplicationandarchitecturemodels.Figure1
rate performance estimations for the multi-processor system
depicts the (cid:3)ow of operation for the Artemis workbench,
architecture as a whole. For this calibration process, the
wherethegreypartsrefertothevarioustool-setsthattogether
Artemis workbench uses the Laura tool-set [49], [60] and
embody the workbench. The point of departure is an appli-
the Molen calibration platform architecture [53](cid:150)[55].Before
cation domain (being multimedia applications for Artemis),
presenting more details on Artemis’ system-level modelling
an experimental domain-speci(cid:2)c platform architecture and a
andsimulationtechniques,whichformsthebulkofthisarticle,
domain-speci(cid:2)c application speci(cid:2)ed as an executable se-
the remainder of this section (cid:2)rst takes a closer look at
quential program. The platform architecture is instantiated the Compaan1 and Laura1 tool-sets as well as the Molen1
in the architecture model layer of the workbench, while
calibration platform.
the application speci(cid:2)cation is converted to a functionally
equivalent concurrent speci(cid:2)cation using a translator called
Compaan [14], [49], [51]. More speci(cid:2)cally, Compaan trans- A. The Compaan and Laura tool-sets
forms the sequential application speci(cid:2)cation into a Kahn Today, traditional imperative languages like C, C++ or
Process Network (KPN) [21]. In between the application and Matlab are still dominantwith respect to implementingappli-
architecture layers there is a mapping layer. This mapping cations for SoC-based (platform)architectures.It is, however,
layer provides means to perform quantitative performance very dif(cid:2)cult to map these imperative implementations, with
analysis on levels of abstraction, and to re(cid:2)ne application typically a sequential model of computation, onto multi-
speci(cid:2)cation components between levels of abstraction. Such processorSoCarchitecturesthatallowforexploitingtask-level
re(cid:2)nement is required to match application speci(cid:2)cations to parallelisminapplications.Incontrast,modelsofcomputation
the level of detail of the underlying architecture models.
Effectively, the mapping layer bridges the gap between the 1Hereweshouldnotethatalthoughasigni(cid:2)cantamountofworkhasbeen
performed on Compaan, Laura and Molen in the context of the Artemis
application and architecture (models), sometimes referred to
project, including the integration of these research efforts into a single
as the implementation gap [35]. In the next sections, we framework,theydonothavetheirorigininArtemis.
Compaan Laura
that inherently express task-level parallelism in applications
andmakecommunicationsexplicit,suchasCSP[20]andPro-
Application P1 P2
cessNetworks[21],[31],allowforeasiermappingontomulti- (Matlab code) Src S1 Sink
processorSoCarchitectures.However,specifyingapplications
P3 P4
using these models of computation usually requires more Transformations Kahn Process Network
(Unroll/Skew/Merge)
implementation effort in comparison to sequential imperative
solutions.
KPNtoArchitecture
Matlab code
In Artemis, we use an approach in which we start from a
sequential imperative application speci(cid:2)cation (cid:150) more specif-
Network of
MatParser
ically an application written in a subset of Matlab (cid:150) which conceptual processors
is then automatically convertedinto a Kahn Process Network
(KPN) [21] using the Compaan tool-set [14], [49], [51]. This SAC Mapping coIPres
conversion is fast and correct by construction. In the KPN library
model of computation, parallel processes communicate with DgParser Network of
eachotherviaunboundedFIFOchannels.Readingfromchan- synthesizable processors
nels is done in a blocking manner, while writing to channels PRDG
is non-blocking. We decided to use KPNs for application Verilog VHDL SystemC
speci(cid:2)cations because they nicely (cid:2)t with the targeted media- Panda
processing application domain and they are deterministic. feedback
The latter implies that the same application input always
Fig.2. TheCompaan (left) andLaura(right)tool-sets.
results in the same application output, irrespective of the
scheduling of the KPN processes. This provides us with a
lot of scheduling freedom when, as will be discussed later
This includes the addition of IP cores that implement certain
on,mappingKPNprocessesontoSoCarchitecturemodelsfor
functionsintheoriginalapplicationaswellassettingattributes
quantitative performanceanalysis.
of componentssuch as bit-width and buffer sizes. In the (cid:2)nal
The infrastructureof the Compaan tool-set is illustrated on
step, the hardware model is converted into VHDL. To do so,
theleft-handsideofFigure2.Thegreypartsrefertothesepa-
Laura supplies a piece of VHDL code for each component
ratetoolsthatarepartofCompaan,whilethewhitepartsrefer
in the hardware model that expresses how to represent that
to the (intermediate) formats of the application speci(cid:2)cation.
componentin the targetarchitecture.Usingcommercialtools,
Starting-pointis anapplicationspeci(cid:2)cationin Matlab,which
the VHDL code can then be synthesized and mapped onto
needs to be speci(cid:2)ed as a parameterised static nested loop
an FPGA. As can be seen in Figure 2, the results from
program. Recently, Compaan’s scope has been extended to
this automated implementation trajectory can be fed back to
also includeweakly-dynamicnestedloopprogramsthat allow
Compaanto exploredifferenttransformationsthat will, in the
forspecifyingdata-dependentbehaviour[47].OntheseMatlab
end, lead to different implementations.
applicationspeci(cid:2)cations,varioussource-leveltransformations
can be applied in order to, for example, increase or decrease
theamountofparallelisminthe(cid:2)nalKPN[48].Inanextstep, B. The Molen calibration platform
the Matlab code is transformed into single assignment code Figure 3 depicts the platform architecture that is used for
(SAC), which resembles the dependence graph (DG) of the componentcalibration in Artemis. This platform architecture,
originalnestedloopprogram.Hereafter,theSAC isconverted called Molen [53](cid:150)[55], connects a programmable processor
to a Polyhedral Reduced Dependency Graph (PRDG) data with a recon(cid:2)gurable unit and uses microcode to incorporate
structure, being a compact mathematical representation of a architectural support for the recon(cid:2)gurable unit. Instructions
DG in terms of polyhedra.Finally, a PRDG is convertedinto
a KPN by associating a KPN process with each node in the
PRDG. The parallel KPN processes communicate with each Main Memory
other according to the data dependencies given in the DG.
TheLauratool-set[49],[60],depictedontheright-handside Instruction Data
fetch Load/Store
of Figure2, takes a KPN as inputand producessynthesizable
VHDL code that implements the application speci(cid:2)ed by the
Data
KPN for a speci(cid:2)c FPGA platform. To this end, the KPN Arbiter Memory
speci(cid:2)cation is (cid:2)rst converted into a functionally equivalent MUX/DEMUX
network of conceptual processors, called hardware model.
This hardware model, which is platform independent as no Register File Core Reconfig.
information on the target FPGA platform is incorporated, Processor Microcode CCU
Unit
de(cid:2)nes the key components of the architecture and their Exchange
Reconfigurable Processor
attributes. It also de(cid:2)nes the semantic model, i.e., how the Registers
various components interact with each other. Subsequently,
platformspeci(cid:2)c informationis addedtothehardwaremodel. Fig.3. TheMolen calibration platformarchitecture.
arefetchedfromthememory,afterwhichthearbiterperforms
A B C Application Model
apartialdecodingontheinstructionstodeterminewherethey Process Network
should be issued [27]. Those instructions that have been im-
event
plementedin (cid:2)xedhardwareare issuedto the coreprocessing trace
(CP)unit,whichis oneofthePowerPCs fromaXilinxVirtex
II ProTM platform in the Molen prototype implementation Mapping Layer
[25], while instructions for custom execution are redirected Dataflow
Virtual Virtual
to the recon(cid:2)gurable unit. The instructions entering the CP Processor Processor
unitarefurtherdecodedandthenissuedtotheircorresponding for Process A for Process C
functional units.
The recon(cid:2)gurable unit consists of a custom con(cid:2)gured
buffer buffer
unit (CCU), currently implemented by the Xilinx Virtex II
ProTM FPGA, and a r(cid:181)-code unit. The recon(cid:2)gurable unit
Architecture Model
performsoperationsthat can beas simpleas aninstructionor Discrete Event
Processor 1 Processor 2 Processor 3
as complex as a piece of code describing a certain function.
FIFO
Molen divides an operation into two distinct phases: set and
bus
execute. The set phase is responsible for recon(cid:2)guring the data channels
CCUhardware,enablingtheexecutionofanoperation.Sucha token−exchange channels Memory
mapping
phasemaybesubdividedintotwosub-phases,namelypartial-
set (p-set) and complete-set (c-set). The p-set phase covers Fig. 4. Sesame’s application model layer, architecture model layer, and
common functions of an application or set of applications. mappinglayerwhichinterfacesbetweenapplication andarchitecture models.
Subsequently, the c-set sub-phase only recon(cid:2)gures those
blocks in the CCU which are not covered in the p-set sub-
Sesamefacilitates performanceanalysis ofembeddedsystems
phase in order to complete the functionality of the CCU.
architectures according to the Y-chart design approach [3],
To perform the actual recon(cid:2)guration of the CCU, recon-
[23],recognisingseparateapplicationandarchitecturemodels
(cid:2)gurationmicrocode is loaded into the r(cid:181)-codeunit and then
withinasystemsimulation.Anapplicationmodeldescribesthe
executed (using p-set and c-set instructions) [26]. Hereafter,
functionalbehaviourofanapplication,includingbothcompu-
the execute phase is responsible for the operation execution
tation and communication behaviour. An architecture model
ontheCCU,performedbyexecutingtheexecutionmicrocode.
de(cid:2)nes architecture resources and captures their performance
Important in this respect is the fact that both the set and
constraints. After explicitly mapping an application model
execute phases do not explicitlyspecify a certain operation to
onto an architecture model, they are co-simulated via trace-
beperformed.Instead,thep-set,c-setandexecuteinstructions
drivensimulation.Thisallowsforevaluationofthesystemper-
point to the memory location where the recon(cid:2)guration or
formanceofaparticularapplication,mapping,andunderlying
execution microcode is stored.
architecture.Essentialinthismodellingmethodologyisthatan
The Compaan and Laura tool-sets in combination with the
application model is independent from architectural speci(cid:2)cs,
Molenplatformarchitectureprovidegreatopportunitiesforthe
assumptions on hardware/software partitioning, and timing
previously discussed calibration of system-level architecture
characteristics. As a result, a single application model can
models. For this purpose, Laura maps a speci(cid:2)c component
be used to exercise different hardware/software partitionings
from an application speci(cid:2)cation to a hardware implemen-
and can be mapped onto a range of architecture models,
tation by converting the Compaan-generated KPN associated
possibly representing different system architectures or simply
with the application component to a VHDL implementation.
modelling the same system architecture at various levels of
This VHDL code is subsequently used as recon(cid:2)guration
abstraction. The layered infrastructure of Sesame is shown in
microcodeforMolen’sCCU,whiletheremainderoftheappli-
Figure 4.
cationspeci(cid:2)cation(i.e.,thecodethathasnotbeensynthesized
to a hardware implementation) is executed on Molen’s core
processor. As a result, the application component mapped
A. Application modelling
ontotheCCUprovideslow-levelimplementationnumbersthat
can be used to calibrate the corresponding component in the For application modelling [12], Sesame uses KPN ap-
system-level architecture model. In Section V, we present a plication speci(cid:2)cations that are generated by the Compaan
case study in which this component calibration is illustrated tool-set or have been derived by hand. The computational
for a DCT task in a Motion-JPEG encoder application. behaviour of an application is captured by instrumenting the
code of each Kahn process with annotations that describe
the application’s computational actions. The reading from
III. THESESAMEMODELLINGANDSIMULATION
or writing to Kahn channels represents the communication
ENVIRONMENT
behaviour of a process within the application model. By
Artemis’ system-level modelling and simulation environ- executingthe Kahnmodel,eachprocess recordsits actionsin
ment, called Sesame [11], [44], builds upon the ground- orderto generateits owntrace of applicationevents,which is
laying work of the Spade framework [34]. This means that necessaryfordrivinganarchitecturemodel.Theseapplication
events typically are coarse grained, such as execute(DCT) or
read(pixel-block,channelid).
ToexecuteKahnapplicationmodels,andtherebygenerating
theapplicationeventsthat representthe workloadimposedon
the architecture,Sesame featuresa processnetworkexecution
enginesupportingKahnsemantics.Thisexecutionengineruns
the Kahn processes as separate threads using the Pthreads
package. Currently, the Kahn processes need to be written
in C++, but C and Java support is also planned for the
future.Toallowforrapidcreationandmodi(cid:2)cationofmodels,
the structure of the application models (i.e., which processes
are used in the model and how they are connected to each
other) is not hard-coded in the C++ implementation of the
processes, but instead, it is described in a language called
YML (Y-chart Modelling Language) [11]. This is an XML-
basedlanguagewhichis similarto Ptolemy’sMoML[30] but
is slightly less generic in the sense that YML only needs
to support a few simulation domains. As a consequence,
Fig. 5. A screenshot of the YML editor, showing its layered layout that
YML supports a subset of MoML’s features. However, YML
correspondstothethreelayersofSesame(seeFigure 4).
provides one additional feature in comparison to MoML as it
contains built-in scripting support. This allows for loop-like
constructs, mapping and connectivity functions, and so on,
YML support. SCPEx raises the abstractionlevel of SystemC
which facilitate the description of large and complex models.
models, thereby reducing the modelling effort required for
Inaddition,itenablesthecreationoflibrariesofparameterised
developing transaction-level architecture models and making
YMLcomponentdescriptionsthatcanbeinstantiatedwiththe
the modelling process less prone to programming errors.
appropriateparameters,therebyfosteringre-useofcomponent
descriptions.TosimplifytheuseofYMLevenfurther,aYML
editorhasalsobeendevelopedtocomposemodeldescriptions
usingaGUI.Figure5givesanimpressionoftheYMLeditor’s C. Mapping
GUI, showing its layered layout that corresponds to the three
layersofSesame(seeFigure4),namelytheapplicationmodel To map Kahn processes (i.e., their event traces) from an
layer, mapping layer and architecture model layer. application model onto architecture model components and
to support the scheduling of application events from differ-
ent event traces when multiple Kahn processes are mapped
B. Architecture modelling
onto a single architecture component (e.g., a programmable
Architecture models in Sesame, which typically operate processor), Sesame provides an intermediate mapping layer.
at the so-called transaction level [10], [19], simulate the This layerconsists of virtualprocessorcomponentsandFIFO
performance consequences of the computation and commu- buffers for communication between the virtual processors.
nication events generated by an application model. These ar- Thereisaone-to-onerelationshipbetweentheKahnprocesses
chitecturemodelssolelyaccountforarchitecturalperformance in the application model and the virtual processors in the
constraints and do not need to model functional behaviour. mapping layer. This is also true for the Kahn channels and
This is possible because the functional behaviour is already the FIFO buffers in the mapping layer, except for the fact
captured in the application models, which subsequently drive that the latter are limited in size. Their size is parameterised
the architecture simulation. An architecture model is con- and dependent on the modelled architecture. As the structure
structed from generic building blocks provided by a library, of the mapping layer closely resembles the structure of the
which contains template performance models for processing applicationmodelunderinvestigation,Sesameprovidesatool
cores, communication media (like busses) and various types that is able to automatically generate the mapping layer from
of memory.The structure of architecture models (cid:150) specifying the YML description of an application model.
which building blocks are used from the library and the way A virtual processor in the mapping layer reads in an
they are connected (cid:150) is also described in YML. applicationtracefromaKahn processvia a trace eventqueue
Sesame’s architecture models are implementedusing either and dispatches the events to a processing component in the
Pearl [38] or SystemC [1], [19]. Pearl is a small but pow- architecture model. The mapping of a virtual processor onto
erful discrete-event simulation language which provides easy a processing component in the architecture model is freely
construction of the models and fast simulation [44]. For our adjustable, facilitated by the fact that the mapping layer and
SystemC architecture models, we provide an add-on library its mapping onto the architecture model are also described in
to SystemC, called SCPEx (SystemC Pearl Extension) [50], YML (and manipulated using the YML editor, see Figure 5).
which extends SystemC’s programming model with Pearl’s Communication channels (cid:150) i.e., the buffers in the mapping
message-passing paradigm and which providesSystemC with layer (cid:150) are also mapped onto the architecture model. In
Figure4,forexample,onebufferisplacedinsharedmemory2 callyderivedfromaKPNapplicationspeci(cid:2)cation,areCDFG-
while the other buffer is mapped onto a point-to-point FIFO like representations of the Kahn processes. They contain
channel between processors 1 and 2. controlconstructslikeCDFGs,butunlikeCDFGs,theyarenot
The mechanism with which application events are dis- directly executable since SPs only contain symbolic instruc-
patched from a virtual processor to an architecture model tions(i.e.,applicationevents)andnorealcode.Therefore,SPs
component guarantees deadlock-free scheduling of the appli- need extra information for execution to determine the control
cation events from different event traces. In this mechanism, (cid:3)owwithinanSP,whichissuppliedintermsofcontroltraces.
computationeventsarealwaysdirectlydispatchedbyavirtual These control traces are generated by running the application
processor to the architecture component onto which it is witha particularset ofdata.At thearchitecturelayer,SPs are
mapped. The latter schedules incoming events that originate executedwiththecontroltracestogenerateeventtraceswhich
from different event queues according to a given policy and aresubsequentlyusedtodrivetheresourcesinthearchitecture
subsequently models their timing consequences. The event model. Like Sesame, Archer also supports the re(cid:2)nement of
schedulingsupportsa rangeof prede(cid:2)nedpolicies, like FCFS architecture models. It does so by transforming application-
andround-robin,but can also easily becustomisedwith alter- level SPs into architecture-levelSPs [56].
nativepolicies.For communicationevents,avirtualprocessor
(cid:2)rst consults the appropriate buffer at the mapping layer to D. Mapping support using multi-objective optimisation
checkwhetherornotacommunicationissafetotakeplaceso
To facilitate effective design space exploration, Sesame
thatnodeadlockcanoccur.Onlyifitis foundtobesafe(i.e.,
provides some (initial) support for (cid:2)nding promising candi-
forreadeventsthedatashouldbeavailableandforwriteevents
date application-to-architecture mappings to guide a designer
thereshouldberoominthetargetbuffer),thencommunication
duringthesystem-levelsimulationstage.Tothisend,wehave
events may be dispatched to the processor component in the
developed a mathematical model that captures several trade-
architecturemodel.As longas a communicationeventcannot
offs faced during the process of mapping [15]. In this model,
be dispatched, the virtual processor blocks. This is possible
we take into account the computational and communication
becausethe mappinglayerexecutesinthesame simulationas
demands of an application as well as the properties of an
thearchitecturemodel.Therefore,boththemappinglayerand
architecture, in terms of computational and communication
thearchitecturemodelsharethesamesimulation-timedomain.
performance, power consumption, and cost. The resulting
Thisalsoimpliesthateachtimeavirtualprocessordispatches
trade-offs with respect to performance, power consumption
anapplicationevent(eithercomputationorcommunication)to
and cost are formulated as a multi-objective combinatorial
a component in the architecture model, the virtual processor
optimisation problem. Using an optimisation software tool,
isblockedinsimulatedtimeuntiltheevent’slatencyhasbeen
which is based on a widely-known evolutionary algorithm
simulated by the architecture model.
[61],themappingdecisionproblemissolvedbyprovidingthe
When architecturemodel componentsare gradually re(cid:2)ned
designer with a set of approximated Pareto-optimal mapping
to includemoreimplementationdetails, thevirtual processors
solutions that can be further evaluated using system-level
at the mapping layer are also re(cid:2)ned. The latter is done with
simulation. For a more detailed description of this mapping
data(cid:3)owgraphssuchthatitallowsustoperformarchitectural
support, the interested reader is referred to [15].
simulationat multiplelevelsofabstractionwithoutmodifying
the applicationmodel. Figure 4 illustrates this data(cid:3)ow-based
IV. ARCHITECTUREMODELREFINEMENTTHROUGH
re(cid:2)nementbyre(cid:2)ningthevirtualprocessorforprocessBwith
TRACETRANSFORMATIONS
a(cid:2)ctivedata(cid:3)owgraph.Inthisapproach,theapplicationevent
tracesspecifywhatavirtualprocessorexecutesandwithwhom Re(cid:2)ning architecture model components in Sesame re-
itcommunicates,whiletheinternaldata(cid:3)owgraphofavirtual quires that the application events driving them should also
processorspeci(cid:2)eshowthecomputationsandcommunications be re(cid:2)ned to match the architectural detail. Since we aim
take place at the architecture level. In the next section, we at a smooth transition between different abstraction levels,
provide more insight on how this re(cid:2)nement approach works re-implementing or transforming (parts of) the application
by explaining the relation between trace transformations for models for each abstraction level is undesirable. Instead,
re(cid:2)nement and data(cid:3)ow actors at the mapping layer. Sesame only maintains application models at a high level
of abstraction (thereby optimising the potentials for re-use of
Inacloselyrelatedproject,calledArcher[58],analternative
application models) and bridges the abstraction gap between
mapping approach is studied. That is, while Archer and
application models and underlying architecture models at the
Sesame share a lot of the same application and architec-
mapping layer. As will be explained in this section, bridging
ture modelling techniques, Archer uses a different mapping
the abstraction gap is accomplished by re(cid:2)ning the virtual
strategy than Sesame. In Archer, Control Data Flow Graphs
processors in the mapping layer with data(cid:3)ow actors that
(CDFG) [59] are taken as a basis. However, as the CDFG
transform coarse-grained application events into (cid:2)ner grained
notation is too complex for design space exploration, the
events at the desired abstraction level which are subsequently
CDFGsareliftedtoahigherabstractionlevel,calledSymbolic
used to drive the architecture model components [16], [17],
Programs (SP) [57]. The SPs, which in Archer are automati-
[42]. In other words, the data(cid:3)ow actors consume external
2The architecture model accounts for the modelling of bus activity (arbi- input(data(cid:3)ow)tokensthatrepresenthigh-levelcomputational
tration,transfers,etc.)whenaccessing thisbuffer. and communication application events and produce external
output tokens that represent the re(cid:2)ned architectural events the architecture events that are the (cid:150) possibly transformed (cid:150)
associated with the application events. representationofapplicationeventsatthearchitecturelevel.In
Re(cid:2)nement of application events is denoted using trace addition, to capture control behaviour of the Kahn processes,
transformations [33], in which the left-hand side contains the IDF graphs also contain dynamic actors for conditional
the coarse-grained application events that need to be re(cid:2)ned jumps and repetitions. The IDF graphs are executable as
and the right-handside the resulting architecture-levelevents. the actors have an execution mechanism called (cid:2)ring rules
Furthermore, ‘(cid:0) ’ symbols in trace transformations denote whichspecify whenan actor can (cid:2)re. When (cid:2)ring an actor,it
the (cid:148)followed by(cid:148) ordering relation. To give an example, consumes the required tokens from its input token channels
the following trace transformations re(cid:2)ne R(ead) and W(rite) and produces a speci(cid:2)ed number of tokens on its output
applicationeventssuchthatthesynchronisationsareseparated channels. A special characteristic of our IDF graphs is that
from actual data transfers [33]: theSDFactorsaretightlycoupledwiththearchitecturemodel
components. This means that a (cid:2)ring SDF actor may send a
Qref
R (cid:1)(cid:3)(cid:2) cd (cid:0) ld (cid:0) sr (1) token to the architecture model to initiate the simulation of
W (cid:1)(cid:3)Qr(cid:2)ef cr(cid:0) st (cid:0) sd (2) an event. The SDF actor in question is then blocked until
it receives an acknowledgement token from the architecture
Here,re(cid:2)nedarchitecture-leveleventscheck-data ,load-data(cid:134),
model indicating that the performance consequences of the
(cid:4)
signal-room , check-room , store-data(cid:134), signal-data are ab- event have been simulated within the architecture model. To
(cid:4) (cid:4) (cid:4)
breviated as cd, ld, sr, cr, st, sd, respectively. The events give an example, an SDF actor that embodies a write event
marked with * refer to synchronisations while those marked
willblockafter(cid:2)ringuntilthewritehasbeensimulatedatthe
with refertodatatransmissions.Theabovere(cid:2)nementsallow architecture level.
(cid:5)
for, for example, moving synchronisation points or reducing In IDF graphs, scheduling informationof IDF actors is not
their number when a pattern of application events is trans-
incorporated into the graph de(cid:2)nition but is explicitly sup-
formed [33], [42]. Consider, for example, an application pro- plied by a scheduler. This scheduler operates on the original
cess that reads a block of data froman input buffer,performs
application event traces in order to schedule our IDF actors.
some computation on it, and writes the results to an output The actor scheduling can be done either in a semi-static or
buffer.Thiswouldgeneratea(cid:148)R(cid:0) E (cid:0) W(cid:148)application-event dynamic manner. In dynamic scheduling, the application and
pattern, in which the E(xecute) refers to the computation on
architecturemodelsareco-simulatedusingaUNIXIPC-based
the block of data. Assuming that this application process is interfacetocommunicateeventsfromtheapplicationmodelto
mappedontoaprocessingcomponentthatdoesnothavelocal the scheduler. As a consequence, the scheduler only operates
storage but operates directly on its input and output buffers,
on a window of application events which implies that the
we need the following trace transformation: IDF graphs cannot be analysed at compile-time. This means
Qref that, for example, it is not possible to decide at compile-time
R(cid:0) E (cid:0) W (cid:1)(cid:6)(cid:2) cd(cid:0) cr(cid:0) ld (cid:0) E (cid:0) st (cid:0) sr(cid:0) sd (3) whether an IDF graph will complete its execution in (cid:2)nite
In the re(cid:2)ned event sequence, we early check (cid:150) using the time,orwhethertheexecutioncanbeperformedwithbounded
check-room (cr) (cid:150) if there is room in the output buffer before memory.Alternatively,wecanalsoscheduletheIDFactorsin
fetching the data (ld) from the input buffer because the a semi-static manner. To do so, the application model should
processingcomponentcannottemporarilystoreresultslocally. (cid:2)rst generate the entire application traces and store them into
In addition, the input buffer must remain available until the trace (cid:2)les (if their size permits this) prior to the architectural
processing component has (cid:2)nished operating on it (i.e., after simulation.Thisstaticschedulingmechanismisawell-known
writingtheresultstotheoutputbuffer).Therefore,thesignal- techniqueinPtolemy[9]andhasbeenproventobeveryuseful
room (sr) is scheduled after the st. for system simulation [8]. However, in Sesame, it does not
In Sesame, Synchronous Data Flow (SDF) [29] actors are yield to a fully static scheduling. This is because of the fact
deployed to realise trace transformations. Integer-controlled that,aswaspreviouslyexplained,ourSDFactorshaveatoken
DataFlow (IDF)[8]actorsaresubsequentlyutilisedto model exchange mechanism with the underlying architecture model,
repetitionsand branchingconditionswhich may be present in yielding some dynamic behaviour.
theapplicationcode[16].However,asillustratedin[42],they WealsointendtoinvestigatewhetherornotourIDFgraphs
mayalso be used withinstatic transformationsto achieveless can be speci(cid:2)ed as so-called well-behaved data(cid:3)ow graphs
complicated (in terms of the number of actors and channels) [18]. In these well-behaved data(cid:3)ow graphs dynamic actors
data(cid:3)ow graphs. are only used as a part of two prede(cid:2)ned clusters of actors (cid:150)
Re(cid:2)ning application event traces by means of data(cid:3)ow knownas schemas (cid:150) that allow for modellingconditionaland
actors works as follows. For each Kahn process at the ap- repetitive behaviour. The resulting graphs have, as opposed
plication layer, an IDF graph is synthesized at the mapping to regular IDF graphs, many of the same attractive properties
layer and embedded in the corresponding virtual processor. withrespecttostaticanalysisasgraphscomposedonlyofSDF
Asaresult,eachvirtualprocessorisequippedwithanabstract actors.
representation of the application code from its corresponding To illustrate how IDF graphs are constructed and applied
Kahn process, similar to the concept of Symbolic Programs foreventre(cid:2)nement,weuseanexampletakenfromaMotion-
from [58]. Sesame’s IDF graphs consist of static SDF actors JPEGencoderapplicationwestudiedin[32]and[44].Figure6
(due to the fact that SDF is a subset of IDF) embodying shows the annotated C++ code for the Quality-Control (QC)
while(1) { ..write(out_TablesInfo,ChrominanceTablesInfo)
read(in_NumOfBlocks,NumOfBlocks); write(out_TablesInfo,LuminanceTablesInfo)
read(in_NumOfBlocks,NumOfBlocks)
// code omitted
write(out_TablesInfo,LumTablesInfo); Scheduler
write(out_TablesInfo,ChrTablesInfo);
switch(TablesChangeFlag) {
case HuffTablesChanged: CASE−BEGIN REPEAT−BEGIN
write(out_HuffTables,LumHuffTables);
write(out_HuffTables,ChrHuffTables);
write(out_Command1,OldTables);
write(out_Command2,NewTables);
break;
case QandHuffTablesChanged:
// code omitted cd cr1 cr2 cr1 cr2 cd
default:
wwrriittee((oouutt__CCoommmmaanndd12,,OOllddTTaabblleess));; ld st1 st2 st1 st2 ld E
break;
}
sr sd1 sd2 sd1 sd2 sr
// code omitted
for(int i=1;i<(NumOfBlocks/2);i++) {
// code omitted
read(in_Statistics,Statistics);
execute("op_AccStatistics"); CASE−END REPEAT−END
// code omitted
}
} Fig.8. IDFgraph fortheQCtaskrealising communication re(cid:2)nement.
Fig.6. AnannotatedC++codefragmenttakenfromaQuality-Control(QC)
taskinaMotion-JPEGencoder application.
token exchange channels for the sake of simplicity. Also not
shown are the token channels to and from the IDF graphs of
neighbouringvirtual processors with which is communicated.
Kahn process of the M-JPEG application. The QC process
For example, the R(ead) actors are in reality connected to
dynamically computes the tables for Huffman encoding as
a W(rite) actor from a remote virtual processor in order to
well as those required for quantising each frame in the video
signalwhendataisavailableandwhenroomisavailable.The
stream, according to the image statistics and the obtained
IDFactorsCASE-BEGIN,CASE-END,REPEAT-BEGIN,and
compression bitrate of the previous video frame. In Figure 7,
REPEAT-ENDmodelconditionalandrepetitionstructuresthat
anIDFgraphfortheQCprocessisgiven,realisingahigh-level
are present in the application code. The scheduler reads the
(unre(cid:2)ned) simulation. That is, the architecture-level events
application trace from the Kahn process in question and
embodied by the SDF actors (depicted as circles) directly
executes the IDF graph by scheduling the IDF actors accord-
represent the application-level R(ead), E(xecute) and W(rite)
ingly by sending the appropriate control tokens. In Figure 7,
events. The SDF actors drive the architecture model compo-
there are (horizontal)dotted token channels between the SDF
nents by the aforementioned token exchange mechanism, al-
actors, denoting dependencies. Adding these token channels
thoughFigure7doesnotdepictthearchitecturemodelnorthe
to the graph results in sequential execution of architecture-
level events while removing them will allow for exploiting
.. parallelism by the underlying architecture model. Like all
write(out_TablesInfo,ChrominanceTablesInfo)
write(out_TablesInfo,LuminanceTablesInfo) models in Sesame, the structure of our IDF graphs is also
read(in_NumOfBlocks,NumOfBlocks)
described using YML.
Scheduler
InFigure8,anIDFgraphfortheQC processisshownthat
implements the aforementioned communication re(cid:2)nement in
CASE−BEGIN REPEAT−BEGIN which the application-level R(ead) and W(rite) events are
re(cid:2)ned such that the synchronisation and data-transfer parts
become explicit. The computational E(xecute) events remain
unre(cid:2)nedinthisexample.Weagainomittedthetokenchannels
to/from IDF graphs of neighbouring virtual processors in
R W1 W2 W1 W2 R E
Figure 8, but in reality cd actors have, for example, an
incoming token channel from an sd actor of a remote IDF
graph. By (cid:2)ring the re(cid:2)ned SDF actors (cd, cr, etc.) in the
IDF graphaccordingtothe orderin whichtheyappearon the
CASE−END REPEAT−END right-hand side of a trace transformation (cid:150) see for example
transformation(3),notingthattheright-handsidemayalsobe
Fig.7. IDFgraph representing the QCtask fromFigure 6,realising high- speci(cid:2)ed as a partial ordering [17], [33] (cid:150) this automatically
level (unre(cid:2)ned)simulation atarchitecture level. yields a valid schedule for the IDF graph [16]. Here, we also
recall that the level of parallelism between the architecture- Main Memory
level events is speci(cid:2)ed by the presence or absence of token
channels between SDF actors. To conclude, communication Instruction Data
fetch Load/Store
re(cid:2)nement is accomplished by simply replacing SDF actors
with re(cid:2)ned ones, allowing to evaluate the performance of Data
Arbiter Memory
differentcommunicationbehavioursatarchitecturelevelwhile MUX/DEMUX
the application model remains unaffected. As shown in [17]
aanlsdolaiklleowwsefwoirllred(cid:2)emnionngsctroamtepiuntathtieonnaelxtbesehcatvioionu,rt.hisapproach Register File ProCcoersesor MRiecUrcooncnitofidge. pixel Preshipftixel
The IDF-based re(cid:2)nement approach also permits mixed- Exchange block In
Registers pixel pixel
levelsimulations,inwhichonlypartsofthearchitecturemodel 2D−DCT Out block
are re(cid:2)ned while the other parts remain at the higher level of CCU
Reconfigurable Processor
abstraction.This will be demonstratedin thenextsectiontoo.
one−to−one
These mixed-level simulations enable performanceevaluation M−JPEG code mapping
of a speci(cid:2)c architecturecomponentwithin the contextof the pixel preshift Kahn Process
in_block Network
behaviourofthewholesystem.Theythereforeavoidtheneed DCT function call in pixel
(in_block, out_block)
forbuildingacompletelyre(cid:2)nedarchitecturemodelduringthe pixel 2d−dct out out_block
earlydesignstages.Moreover,mixed-levelsimulationsdonot pixel
suffer from deteriorated system evaluation ef(cid:2)ciency caused
by unnecessarily re(cid:2)ned parts of the architecture model.
Fig.9. Calibration ofthe DCTtask usingthe Compaan/Laura frameworks
V. MOTION-JPEG CASESTUDY andtheMolen calibration platform.
This section presentsan experimentthat illustrates some of
theimportantaspectsofArtemis’(cid:3)owofoperationasdepicted
inFigure1.Morespeci(cid:2)cally,usingtheMotion-JPEGencoder For the system-level modelling and simulation part of
application from the previous section, we demonstrate how the experiment, we decided to model the Molen calibration
componentcalibrationcanbeperformedbymeansoftheCom- platform architecture itself. This gives us the opportunity to
paan/Lauratool-setsandtheMolenplatform.Furthermore,we actually validate our performanceestimations against the real
describe the system-level modelling and simulation aspects numbersfrom the implementation.The resulting system-level
of the M-JPEG experiment, emphasising on the IDF-based Molen model contains two processing components (Molen’s
architecture model re(cid:2)nement that was performed. CP and CCU) whichare bi-directionallyconnectedusing two
In the experiment, we selected the DCT task from the M- uni-directional FIFO buffers. Like in the real Laura(cid:0) Molen
JPEGapplicationforcalibration.ThismeansthattheDCTtask mapping, we mapped the DCT Kahn process from our M-
is taken (cid:148)all the way down(cid:148) to a hardware implementation in JPEG application model onto the CCU component in the ar-
order to study its low-level performance aspects. To do so, chitecturemodel,whereastheremainingKahnprocesseswere
the followingsteps were taken, which are integrallyshown in mapped onto the CP component. We also decided to re(cid:2)ne
Figure 9. The DCT was (cid:2)rst isolated from the sequential M- the CCU in our architecture model such that it models the
JPEG codeandusedasinputtotheCompaantool-set.Subse- pixel-level DCT implementation used in the Compaan/Laura
quently, Compaan generated a KPN application speci(cid:2)cation implementation.TheCP componentinourarchitecturemodel
for the DCT task. This DCT KPN is internally speci(cid:2)ed at was not re(cid:2)ned, implying that it operates (i.e., models timing
pixel level but has in- and output tasks that operate at the consequences) at the same (pixel-block) level as the appli-
levelofpixelblocksbecausetheoriginalM-JPEGapplication cation events it receives from the application model. Hence,
speci(cid:2)cationalsooperatesatthisblocklevel.UsingtheLaura this yields a mixed-level simulation. We would also like to
tool-set, the KPN for the DCT task was translated into a stress that the M-JPEG application model was not changed
VHDL implementation, in which for example the 2D-DCT forthisexperiment.Thismeansthattheapplicationeventsfor
component is implemented as a 92-stage pipelined IP block, the CCU component, referring to DCT operations on entire
that can be mapped onto the FPGA (i.e., CCU) of the Molen pixel blocks, needed to be re(cid:2)ned to pixel-level events. In
platform. By mapping the remainder of the M-JPEG code addition,atthearchitecturemodellevel,theexecutionofthese
onto Molen’s core processor (CP), we were able to study pixel-level events required to be modelled according to the
the hardware DCT implementation in the context of the M- pipelined execution semantics of the actual implementation.
JPEG application. As will be explained later, the results of This because the Preshift and 2D-DCT blocks in the Laura-
this exercise have been used to calibrate our system-level generated implementation are pipelined units.
architecture modelling. Although being out of scope for this According to what was explained in the previous section,
article,itmightbeworthmentioningthattheM-JPEGencoder weaccomplishedthere(cid:2)nementoftheCCUcomponentinthe
with FPGA-implemented DCT obtained a 2.14 speedup (cid:150) architecturemodelbyre(cid:2)ningthevirtualprocessorassociated
out of a 2.5 maximum attainable theoretical speedup (cid:150) in withtheDCTKahnprocessinthemappinglayer,asthisisthe
comparison to a full software implementation. virtualprocessorthatismappedontotheCCUcomponent.The