mipi devcon 2016: mobile user interface aggregation for heterogeneous compute intensive solutions
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Mobile User Interface
Aggregation for Heterogeneous
Compute Intensive Solutions
Abdullah Raouf Lattice Semiconductor
Agenda • Data Capture
• Sensors available • Use case mapping
• Data Transfer • MIPI interface options • Value of using FPGA to transfer data via MIPI interfaces
• Data Analysis • Analytics trends • MHC architectures • MHC solutions used every day
• FPGA Architecture & Solutions
2
Capturing Data
Audio Sensors: Voice recognition, phrase detection, location sensing
4
Image Sensors: Gesture, eye tracking, proximity, depth, movement
Powerefficientheterogeneouscompu2ngneededforsensor
s2tchingandreal-2mefeedback
Health Sensors: EKG, EEG, EMG, temp
Other Sensors: Gyro, compass, accelerometer, magnetometer, light
Real tim
e sensor feedback
Always aware and worki
ng
Iris ▪ Fingerprint ▪ Motion ▪ Image ▪ EKG
▪ Gyro ▪ Audio ▪ Accelerometer ▪ EEG ▪
Mag
neto
met
er ▪
Ges
tur
e ▪ EMG ▪
A Variety of Interface Needs Exist
5
analog
DSI
I2SYUVSLVS
I2CSPI
I3C
D-PHY
C-PHY
SLVS-ECHiSPi
CSI-2
PCMsLVDS
PDM
SLIMbusVirtualChannels
RFFE UART
RAW
proprietary
analog PDM SoundWire
SLIMbusI2S
D-PHY YUV RGB SPI C-PHY DSI
CSI-2 sLVDS SLVS I3C
PCM
Bridge/Aggregate Easily with FPGAs
6
SOC/AP
SoundWire iCEFamily
PDM I2S
UART
I2C/SPI I3C
CSI-DSI
SLVS LVDS
121balls36balls 16Balls
80balls
1.4mmx1.4mm6mmx6mm
MIPI Transfer Optimization
7
… Microphones
Speakers
Displays
Cameras
SOC/AP Sensors
UseCase MIPIStandard
Display MIPIDSI
Imagesensor MIPICSI-2
Genericsensoraggrega2on SPI,I2C,UART,GPIO!MIPIRIO,MIPII3C
Audio MIPISoundWire,MIPISLIMbus
RF MIPIRFFE
BaWery MIPIBIF
Contextuallyawarecompu2ngAnalyzingData
Facerecogni2on
Voicerecogni2on
Backgroundremoval
MHC
Gesturerecogni2on
TradiHonalAlgorithmsvsNeuralNetworks
TradiHonalAlgorithms
• Basedonpre-conceivedevents• Usedtofindexactorapproximatesol’n• Domainexper2serequired• Architecture&dataflowexplicitlydefined• Func2onallycomprehensible• ImplementedinSW
NeuralNetwork
• Basedonexperienceprobabili2es• UsedtofindpaWernsindata• Structureanddataflowevolveswithinthe
networkbasedontraining• CanbeimplementedinSW(GPUs)orHW
(DSPsandFPGAs)
Enabledby:" Moore’sLaw" Voltagescaling
Constrainedby:× Power× Complexity
Enabledby:" Moore’sLaw" Voltagescaling
Constrainedby:× Power× ParallelSW× Scalability
Enabledby:" Abundantdataparallelism" Powerefficiency" Repe22vecomputa2on
Constrainedby:× Programmingtools/models× Highcostofentry
Programmability
Repe22veComputa2onalQuickness
Enabledby:" Timetomarket" FlexibleI/Os" Accelerators" Parallelprocessing" Powerefficiency" Repe22vecomputa2on
Constrainedby:× Programmingtools
MobileHeterogeneousCompuHngArchitectures
Micro-ProcessorAdvancement
Single-CoreProcessor
Mul2-CoreProcessor
HeterogeneousProcessor
HETEROGENEOUSCOMPUTING
FPGA
MHC
HOMOGENEOUSCOMPUTING
Complex Functions Always-onsensordataAcous2cbeamforming
FeatureRich>5KLUTs>6xDSPs>1MbRAM
FPGARangeofMobileSoluHons
Simple Functions MIPIRFFEantennatuning,RGBLED,Levelshieing,etc.
Mid Range FunctionsPedometer,FFTs,co-processing,etc.
MidRangeFeatureSet>3KLUTs>2xDSPs
>100KbSRAM
SimpleFeatureSetSub2KLUTs
Sub80KbRAM
1.4x1.4mm 2.08x2.08mm
>21IOs10IOs
2.15x2.55mm
>21IOs
2.5x2.5mm
>21IOs
Machine Vision 4Kvideotransferwith
MIPID-PHY
HighSpeedI/O>5KLUTs>6xDSPs
HardD-PHY
Summary
FPGAsarechangingthewayyouinteractwithyourdevices
• Energy-efficientparallelprocessingforrepe22venumbercrunchingneededforMHC
• Embeddedmemoryenablesimplementa2onofalways-onsensordatabuffers
• Supportsavarietyofbridging,bufferinganddisplayapplica2ons(FlexibleI/Os)
• AcceleratekeyinnovaHonsinnext-genera2onmobileandindustrialproducts
• CarpeDatumakaseizethedata• Datahasnovalueifitisnotcaptured,transferredandanalyzedcorrectly• UseMIPII/OandLajcepre-processingsolu2onstotrulyseizevalueoutofdata!
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