HandheldDeviceArchitectures:AreWeDoingEnough?

ManuAwasthiAshokaUniversity

HandheldDevices

AndroidVersions

https://www.counterpointresearch.com/can-android-o-de-fragment-android/

MemoryUsage

https://eitik.com/17-android-browser-tested-for-memory-usage-in-2018/

AFewTrends

5

MobileArchitectureResearch● Mobile computing research: 1% of research papers published eachfocus on mobile computing.

● Lack of tools

V.J.Reddi,H.Yoon,andA.Knies,“Twobilliondevicesandcounting,”IEEEMicro,vol.38,no.1,pp.6–21,January/February2018.

Whatisneeded?

• ToolandSimulators

• Benchmarks

BBench(Michigan)

AsimBench(ICT,China)

CurrentStatus

AndroidEmulator

AndroidOpenSourceProject(AOSP)

AndroidVirtualDevice(AVD)

AndroidEmulatorDeviceBehavior

HowdoweleveragetheexistingecosystemtostudymemorybehaviorofAndroidapps?

META:ToolDesign

MobiCom2018

TracerModule- ModificationsinQEMU

ModifiedTranslationPath

RawTraces

META:ToolDesign :CacheModule

CacheSimulationModule

L1andL2CachehitratesavailableafterrunningacalculatoronarangeofandroidversionsfromAndroid4(Kitkat)toAndroid7(Nougat).

META:ToolDesign:MemoryModule

NVMainIntegration

● NVMain :cycle-levelmainmemorysimulator

● CansimulateDRAM,emergingnon-volatilememoriesatthearchitecturallevel.○ Phasechangememory,STT-RAM

PotentialUse-cases

● TraceGeneration○ Thetracescanalsobeusedtoanalyzeinstructiondistributionprofile.○ Creationofsyntheticinputstomodelsbasedonrealinstructionprofiles

● CacheHierarchyModeling○ Acustom, N-levelcachehierarchy

● DRAM,Non-volatile,HybridMemorySimulation○ NVMain canmodelmosttechnologies

TrendsinHandheldDevices

18

https://thehackernews.com/2015/09/6gb-ram-smartphone.html

http://www.es.ele.tue.nl/~kgoossens/Chandrasekar14PHD.pdfhttps://www.pwc.com/gx/en/technology/mobile-innovation/assets/pwc-dram-memory.pdf

HandheldsandSmartphones

19HexagonDSP:AnArchitectureOptimizedforMobileMultimediaandCommunications,IEEEMicro,Vol34,2014

MainMemoryinHandhelds

2020

MC

Core1 Core2

Core3Core4

Fabric Fabric

GPU

Camera

Display

Audio

Sensors

Modem

DRAM DRAM

HandheldApplications

21

Iffrequentlyaccesseddatacanbeconcentratedtothefastestregionsofahybridmemoryhierarchy,memory

system’senergyconsumptioncanbereducedsignificantly,without anysignificantlossinperformanceanduser

experience.

HybridMemoryArchitecturesforHandhelds

TotalPhysicalAddressSpace0GB 4GB

SubAddressSpace SubAddressSpace SubAddressSpace

DRAMMemTech2NVM

HybridMainMemoryinHandhelds

2323

MC

Core1 Core2

Core3Core4

Fabric Fabric

GPU

Camera

Display

Audio

Sensors

Modem

DRAM NVM

MC

DATE2018

HybridMainMemoryinHandhelds

24

MC

Core1 Core2

Core3Core4

Fabric Fabric

GPU

Camera

Display

Audio

Sensors

Modem

DRAM

NVM

MC MC MC

NVM

DRAM

DATE2018

Results

25

Results– 4Controllers

26

KeyTakeways

• Researchintohandhelddevicesarchitecturesisimportant,moresointheeraofwearables• Memorysub-systemisbecomingincreasinglyimportant,eveninhandheld• Needtools,benchmarkstocarryresearchforward• META– onestepinthatdirection

• NVMswilleventuallybeintegratedintomemoryhierarchy• Mechanismstoprovideaccesstohighcapacity,lowlatencymemoriesmightrequireintelligentdatamanagement• H/W– S/Wco-designisbetterthanoneortheother.

27

Acknowledgements

VarunGohil,Sneha Ved (IITGandhinagar)

NisargParikh(LDCollegeofEngineering)