Reliable and Energy Efficient Streaming Multiprocessor Systems

158 Pages·2018·3.34 MB·English

by  Anup Kumar Das

Embedded Systems
Anup Kumar Das
Akash Kumar
Bharadwaj Veeravalli
Francky Catthoor
Reliable and
Energy Effi cient
Streaming
Multiprocessor
Systems
Embedded Systems
Serieseditors
NikilD.Dutt,Irvine,CA,USA
GrantMartin,SantaClara,CA,USA
PeterMarwedel,Dortmund,Germany
This Series addresses current and future challenges pertaining to embedded
hardware, software, specifications and techniques. Titles in the Series cover a
focused set of embedded topics relating to traditional computing devices as well
as high-tech appliances used in newer, personal devices, and related topics. The
material will vary by topic but in general most volumes will include fundamental
material(whenappropriate),methods,designsandtechniques.
Moreinformationaboutthisseriesathttp://www.springer.com/series/8563
Anup Kumar Das • Akash Kumar
Bharadwaj Veeravalli • Francky Catthoor
Reliable and Energy Efficient
Streaming Multiprocessor
Systems
123
AnupKumarDas AkashKumar
ElectricalandComputer ChairforProcessorDesign
EngineeringDepartment DresdenUniversityofTechnology
DrexelUniversity,BossoneResearch Dresden,Germany
EnterpriseCenter
Philadelphia,PA,USA FranckyCatthoor
IMEC
BharadwajVeeravalli Heverlee,Belgium
DepartmentofECE
NationalUniversityofSingapore
Singapore,Singapore
ISSN2193-0155 ISSN2193-0163 (electronic)
EmbeddedSystems
ISBN978-3-319-69373-6 ISBN978-3-319-69374-3 (eBook)
https://doi.org/10.1007/978-3-319-69374-3
LibraryofCongressControlNumber:2017956340
©SpringerInternationalPublishingAG2018
Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpartof
thematerialisconcerned,specificallytherightsoftranslation,reprinting,reuseofillustrations,recitation,
broadcasting,reproductiononmicrofilmsorinanyotherphysicalway,andtransmissionorinformation
storageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilarmethodology
nowknownorhereafterdeveloped.
Theuseofgeneraldescriptivenames,registerednames,trademarks,servicemarks,etc.inthispublication
doesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfromtherelevant
protectivelawsandregulationsandthereforefreeforgeneraluse.
Thepublisher,theauthorsandtheeditorsaresafetoassumethattheadviceandinformationinthisbook
arebelievedtobetrueandaccurateatthedateofpublication.Neitherthepublishernortheauthorsor
theeditorsgiveawarranty,expressorimplied,withrespecttothematerialcontainedhereinorforany
errorsoromissionsthatmayhavebeenmade.Thepublisherremainsneutralwithregardtojurisdictional
claimsinpublishedmapsandinstitutionalaffiliations.
Printedonacid-freepaper
ThisSpringerimprintispublishedbySpringerNature
TheregisteredcompanyisSpringerInternationalPublishingAG
Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland
Thisbookisdedicatedtothosewhocontinue
thepursuitofknowledge,despitethe
obstacleslifepresents.Youholdthekeyto
ourfuture.MayGodblessyouwith
determinationonyourjourney.Neverlet
thosedoubtsornegativityruinyourspirit.
Besteadfastinyourquestforknowledge.
GodBless.
Preface
As the performance demands of applications (e.g., multimedia) are growing,
multiple processing cores are integrated together to form multiprocessor systems.
Energy minimization is a primary optimization objective for these systems. An
emerging concern for designs at deep-submicron technology nodes (65nm and
below)isthelifetimereliability,asescalatingpowerdensityandhencetemperature
variation continues to accelerate wear-out leading to a growing prominence of
device defects. As such, reliability and energy need to be incorporated in the
multiprocessordesignmethodology,addressingtwokeyaspects:
• lifetimeamelioration,i.e.,improvingthelifetimereliabilitythroughenergy-and
performance-awareintelligenttaskmapping
• graceful degradation, i.e., determining the task mapping for different fault-
scenarios while minimizing the energy consumption and providing a graceful
performancedegradation
Inthisbook,aplatform-baseddesignmethodologyisfirstproposedtominimize
temperature-related wear-outs. Fundamental to this methodology is a temperature
modelthatpredictsthetemperatureofacoreincorporatingnotonlyitsdependency
onthevoltageandfrequencyofoperation(temporaleffect),butalsoitsdependency
onthetemperatureofthesurroundingcores(spatialeffect).Theproposedtemper-
aturemodelisintegratedinagradient-basedfastheuristicthatcontrolsthevoltage
and frequency of the cores to limit the average and peak temperature leading to a
longerlifetime,simultaneouslyminimizingtheenergyconsumption.
A design flow is then proposed as a part of the hardware-software co-design
methodologytodeterminetheminimumnumberofcoresandthesizeoftheFPGA
fabricofareconfigurablemultiprocessorsystem.Theobjectiveistomaximizethe
lifetime reliability of the cores while satisfying a given area, performance, and
energy budget. The proposed flow incorporates individual as well as concurrent
applicationswithdifferentperformancerequirementsandthermalbehaviors.While
theexistingstudiesdetermineplatformarchitectureforenergyandareaminimiza-
tion, this is the first approach for reconfigurable multiprocessor system design
consideringlifetimereliabilitytogetherwithmulti-applicationuse-cases.
vii
viii Preface
Toprovidegracefulperformancedegradationinthepresenceoffaults,areactive
fault-tolerance technique is also proposed that explores different task mapping
alternatives to minimize energy consumption while guaranteeing throughput for
all processor fault-scenarios. Directed acyclic graphs (DAGs) and synchronous
data flow graphs (SDFGs) are used to model applications making the proposed
methodologyapplicabletostreamingmultimediaandnon-multimediaapplications.
Fundamental to this approach is a novel scheduling algorithm based on self-
timedexecution,whichminimizesboththeschedulestorageoverheadandrun-time
scheduleconstructionoverhead.Unliketheexistingapproacheswhichconsidertask
mappingonly,theproposedtechniqueconsiderstaskmappingandschedulinginan
integratedmanner,achievingsignificantimprovementwithrespecttothesestate-of-
the-artapproaches.
Finally, an adaptive run-time manager is designed for lifetime amelioration
of multiprocessor systems by managing thermal variations, both within (intra)
and across (inter) applications. Core to this approach is a reinforcement learning
algorithm, which interfaces with the on-board thermal sensors and controls the
voltage and frequency of operation and the thread-to-core affinity over time. This
approach is built on top of the design-time analysis to minimize learning time,
addressrun-timevariability,andsupportnewapplications,makingtheoverallbook
objective to provide a complete and systematic design solution for reliable and
energy-efficientmultiprocessorsystems.
Texas,UnitedStates AnupKumarDas
Dresden,Germany AkashKumar
Singapore,Singapore BharadwajVeeravalli
Heverlee,Belgium FranckyCatthoor
Contents
1 Introduction .................................................................. 1
1.1 TrendsinMultiprocessorSystemsDesign............................. 1
1.1.1 TrendsinTransistorScaling.................................... 1
1.1.2 TrendsinMicroprocessorDesign.............................. 2
1.1.3 TrendsinMultiprocessorSystems............................. 4
1.2 MultiprocessorSystemClassification.................................. 6
1.2.1 Memory-BasedClassification.................................. 6
1.2.2 Processor-BasedClassification................................. 7
1.3 MultiprocessorSystemDesignFlow................................... 8
1.3.1 Platform-BasedDesign......................................... 10
1.3.2 Hardware–SoftwareCo-design ................................ 10
1.4 DesignChallengesforMultiprocessorSystems....................... 11
1.4.1 EnergyConcernforMultiprocessorSystems ................. 11
1.4.2 ReliabilityConcernforMultiprocessorSystems.............. 12
1.5 ReliableandEnergyEfficientMultiprocessorDesign ................ 13
1.5.1 Design-TimeMethodology..................................... 13
1.5.2 Run-TimeMethodology........................................ 15
1.6 KeyHighlightsofThisBook........................................... 16
References..................................................................... 18
2 OperationalSemanticsofApplicationandReliabilityModel .......... 23
2.1 ApplicationModelasSynchronousDataFlowGraphs............... 23
2.2 Wear-OutRelatedReliabilityModel................................... 24
2.2.1 Device-LevelReliabilityModeling............................ 24
2.2.2 Core-LevelReliabilityModeling .............................. 27
2.2.3 System-LevelReliabilityModeling............................ 29
References..................................................................... 31
ix
x Contents
3 LiteratureSurveyonSystem-LevelOptimizationsTechniques......... 33
3.1 Design-TimeBasedReliabilityandEnergyOptimization............ 33
3.1.1 TaskMappingApproachesforPlatform-BasedDesign....... 33
3.1.2 ExistingApproachesonHardware–SoftwareCo-design..... 36
3.1.3 ExistingApproachesonReactiveFault-Tolerance............ 36
3.2 Run-TimeBasedReliabilityandEnergyOptimization ............... 38
References..................................................................... 40
4 ReliabilityandEnergy-AwarePlatform-BasedMultiprocessor
Design ......................................................................... 45
4.1 Introduction ............................................................. 45
4.2 ProblemFormulation ................................................... 46
4.2.1 ApplicationModel.............................................. 46
4.2.2 ArchitectureModel............................................. 47
4.2.3 MappingRepresentation........................................ 47
4.2.4 MTTFComputation ............................................ 48
4.2.5 EnergyComputation............................................ 49
4.2.6 Reliability-EnergyJointMetric................................ 51
4.3 ProposedTemperatureModel .......................................... 51
4.4 ComputingTemperaturefromaSchedule............................. 55
4.5 DesignMethodology.................................................... 57
4.5.1 ReliabilityOptimizationforIndividualApplication.......... 59
4.5.2 ReliabilityOptimizationforUse-Cases........................ 60
4.6 ExperimentsandDiscussions........................................... 62
4.6.1 TimeComplexity ............................................... 62
4.6.2 ValidationoftheTemperatureModel.......................... 64
4.6.3 ComparisonwithAccurateTemperatureModel .............. 65
4.6.4 ImpactofTemperatureMisprediction ......................... 66
4.6.5 MTTFImprovementConsideringTaskRemapping .......... 67
4.6.6 ReliabilityandEnergyImprovement .......................... 70
4.6.7 Use-CaseOptimizationResults................................ 72
4.7 Remarks ................................................................. 73
References..................................................................... 73
5 ReliabilityandEnergy-AwareCo-designofMultiprocessorSystems.. 75
5.1 Introduction ............................................................. 75
5.2 Reliability-AwareHardware–SoftwareTaskPartitioning............. 77
5.2.1 ApplicationandArchitectureModel........................... 78
5.2.2 ReliabilityModelingConsideringSingleActor............... 80
5.2.3 ReliabilityModelingwithMultipleInterconnectedActors... 82
5.2.4 LifetimeReliabilityandTransientFaultReliability
Trade-Off........................................................ 83
5.2.5 Hardware–SoftwarePartitioningFlow......................... 85