AN EVOLUTION OF MOBILE GRAPHICS, V2
Michael C. Shebanow
Vice President, Advanced Processor Lab
Samsung Electronics

October 10, 2014

1

DISCLAIMER• The views herein aremy own• They do not represent
Samsung’s vision norproduct plans

2• Computing History• The Mobile Market• Review of GPU Tech
• GPU Efficiency• User Experience• Tech Challenges• Summary3The past and the trajectory into the future…

ON THE HORIZON
4WHERE HAVE WE BEEN?

4/7/1964
IBM 360 Announced

1976
1/1/1991
Cray 1 Delivered(MECL, 166 MFLOPs) Initial 2G (GSM) Cell Phones
4/12/2012
Google Glass announced

3/31/1951
UNIVAC I Delivered(vacuum tubes, mag tape)(1.9 Kops/sec)

1965
PDP8 Announced(~800 Kops/sec)

May 1982
Sun-1 Launched(M68000)

1950

6/29/2007
7/12/1999 iPhone released (USA)RIM 870 Intro

2050

Jan 1984
Apple Mac announced
Jan 1975
MITs Altair 8800
(Intel 8080)

6/14/1956
TX0 Operational(3.6K Transistors,256x256 memory)
1965
CDC 6600(~3 Mops/sec)

9/19/2014***TODAY***
6/1/2000
AMD Athlon Thunderbird(1 GHz X86)

8/12/1981
IBM PC Intro

5

CLIENTS• Diversity• Phones, tablets, laptops• Wearables• IOT• Cloud integration• The internet at yourfingertips

DRIVERS FOR CLIENT COMPUTERARCHITECTURE?
• PPA (performance, power, area)• Specialization (fixed function)• Phones Phone• Phones Interact• Cameras camera• Refrigerators refrigerate• Rapid time-to-market

CLOUD• PPA again the big driver• Aka, perf/watt/$• Virtualization• Security• Storage Architecture• Flash, cheap disks, IOPs
• SANs• Connectivity• 5G, WiFi, BlueTooth, NFC• Copper, Fiber

The Rise of the Mobile GPU & Connectivity

A NEW WORLD COMING?
9DISCRETE GPU MARKET
Tailing Off

10

MOBILE GPU MARKET• In 2013, an estimated 1.2B+
mobile GPUs shipped• ~200M tablets• ~1B smart phones

SmartPhones“Phablets”Tablets• Continues to grow, but
saturation on the horizon?

• Trend:• Discrete GPU decreasing• Mobile is growing

Gartner: 195m-tablets-sold-in-2013-android-grabs-top-spot-from-ipad-with-62-share/
Source: IDC Worldwide Mobile Phone Tracker, January 27, 201411

WW INTERNET TRAFFIC• Internet traffic growthrate is staggering• 2013 total traffic
estimated at ~51.1 EB
per month• By 2018, more thandouble• 2014 per person smart
phone traffic at~8 GB per month on
WiFi and ~3 GB per
month on broadband• 2014 per person tablet
traffic less, but still ~3.2
GB per month WiFiand ~0.4 GB permonth broadband

12WHERE ARE WE HEADED FROM A HWPERSPECTIVE?…

• Enormous quantity of GPUs
• Large amount ofinterconnectivity• Better I/O

13

GPU Pipelines

A BRIEF REVIEW OF GPU TECH
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MOBILE GPU PIPELINE ARCHITECTURES
Tile-based immediate
mode rendering(TBIMR)
Tile-based deferredrendering(TBDR)IAVSCCVIAVSCCVRSPS

ROPscene

RSPS

ROP

IA = input assembler
VS = vertex shader
CCV = cull, clip, viewport transform
RS = rasterization, setup
PS = pixel shader
ROP = raster operations (blend)
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TBDR W/ HSR• HSR = hidden surface
removalFirst
OpaqueObject• Sort all objects acrosseach projection rayProjection
Ray• Use tiling to reduce data
set size• Only nearest opaque andcloser transparentobjects need to be drawn• Remaining fragments
can be killed => notdrawn

PixelProjection
Plane

Focal Point

16

MOBILE GPULANDSCAPECompanyProductPipeline

Notes

ARM

Mali

TBIMR

Unified shader, 2-4 math pipes per core

Imagination

PowerVR

TBDR/HSR

Latest is Series6XT. Unified shader. DX11 support

Qualcomm

AdrenoFlexRender

Unified shader. “FlexRender” = automatic switching between direct
render (IMR) and tile-based deferred rendering (TBDR).

NVIDIATegra

TBDR &TBIMR

Evolution:• Tegra 1/2/3/4: non-unified TBDR architecture• Logan: Kepler-based GPU, TBIMR• Parker: Maxwell-based GPU, TBIMR

VivanteScalarMorphic

IMR

Unified Shader.

IntelGen| AtomIMR| PowerVR

Market leader in integrated graphics.
Atom-based devices using Imagination PowerVR

AMDRadeon

IMR

Mobile R9 M2xx series (2014)17

Efficiency

A PATH TO A BETTER MOBILE GPU?
[PART 1]18

WHAT IS IMPORTANT?

• More with less
• Better user experience

19

PARALLELISM• Parallel vs. Sequential• Parallel  independence• Sequential  dependence• Three fundamental forms ofparallelism• Spatial: executing operations
between threads at the same time
• Temporal: executing operations
between threads at the sameplace• ILP: executing operations fromwithin the same thread in parallel• Fundamental differences
between ILP-only machines andmassive TLP-ILP machines• CPUs vs. GPUs
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THROUGHPUT VS. LATENCY• Throughput = rate at which
operations complete• Latency = time it takes tocomplete an operation or set ofoperations• CPUs versus GPUs
• In CPUs, the primary objective islow latency• In GPUs, the primary objective ishigh throughput• CPUs versus GPUs
• In an application suitable forCPUs, we assume a low degreeof TLP• In an application suitable forGPUs, we assume a high degreeof TLP
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GPU PERFORMANCE•

Supply and demand:𝑆 ≥ 𝜆𝐷(“limiter equation”)•Lambda (𝜆) is throughput•

Supply examples:••

FP BW (flops/clock)•Texture BW (quads/clock)•Memory BW (bytes/clock)

Demand density examples:•FP ops per shader•

Sample ops per shader

22

POWER EFFICIENCY• Performance = power
efficiency• Two types of efficiency:• “perf@watts”:
The ability to deliver maximum
performance• “watts@perf”:
The ability to deliver maximum
battery life at a minimum
required performance
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WHAT IS EFFICIENCY?
•

Perf @ Watts• Maximum performance at somepower limit

Pidd• Limits:• electrical (Pidd)• die temp (Tj)
• skin temp (Tcase)• battery life (Pbat)• Watts @ Perf
• Minimum power at constantperformance

Tj

Pow
er

Efficiency
Envelope

Perf @ watts

Tcase
Pbat
Watts @ perfTime• Example: deliver 60 frames/sec
at lowest power
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ENERGY REDUCTION TECHNIQUES• Work Reduction• Memory Avoidance• Memory BW Reduction• Memory Access
Management25

WORK REDUCTION• Pixel shaders in ES games~95% of the shader load
• A pixel shader killed is rawpower savings• HSR can kill 30-50% of theshader threads• Geometry in DX11 a problem• Unigine Heaven ~10MTri/frame• Can be up to 70% of shaderworkload• Inter-frame work reduction?
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RELATIVE ACCESS ENERGY COSTS
4000x

10000x

640x

1000x
1000x

120x

160x

Energy/byte

100x

Energy/op

16x

10x

4x

4x
1x

1x
LPDDR

WIO1SRAM

Small RFSP FMA
27

MEMORY AVOIDANCE• Memory power a problem• LPDDR ~100 pJ/byte(100 mW @ 1 GB/sec)• WIO1 ~24 pJ/byte(24 mW @ 1 GB/sec)• On-chip SRAM ~0.6 pJ/byte(0.6 mW @ 1 GB/sec)• Reduction in working set for nonessential traffic (i.e., not texture,attribute, command, or render target)• Rematerialize? (computation vs. BW)• Scheduling to reduce lifetimes?
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MEMORY BW REDUCTION• Texture compression (RD)• Better compression?
• Tessellation use of textures?

• Tile compression (WT)• TB-based signature checking• Lossless compression• Attribute compression (RD)• Reduce stream BW

29

MEMORY ACCESS MANAGEMENT• SOC memory architecture• Blood rivals (antagonists)• Effect of CPU/GPU traffic on
Memory Controller (MC)• Intelligent page open/closemanagement• Balance latency vs. BW• Mismanaging DRAM resultsin both performance loss
AND extra energy – double
whammy30

A better user experience…

A PATH TO A BETTER MOBILE GPU?
[PART 2]31

USER EXPERIENCE (UX)

• User Experience =perception of device:• Functionality• Integration into every
day life• Ease of use(intuitive)
ISO 9241-210[1] defines user experience as "a person's perceptions and responses that result from the use or anticipated use of a product,system or service". - Wikipedia
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APPLICATION: NAVIGATION

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APPLICATION: FACE RECOGNITION34APPLICATION: TELEPRESENCE

http://www.youtube.com/watch?feature=player_detailpage&v=Nzi0sm81tP4"General-Purpose Telepresence with Head-Worn Optical See-Through Displays and Projector-BasedLighting." by Maimone A., Yang, X., Dierk, N., State, A., Dou, M., and Fuchs, H. , IEEE Virtual Reality 2013

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APPLICATION: VIRTUAL COMPUTER

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THE UX OPPORTUNITY• Killer apps will beintegration of:• AR/MR technology• Big Data operations• Subject to:• Real-time constraints• Parallelization on amassive scale37

Making a better UX

FUTURE MOBILE TECH CHALLENGES?
38

KEY CHALLENGES• I/O:• AR Headsets• Environment Imaging• IOT integration• Computational:• API Improvements• Cloud-device integration

39

AR HEADSETS• Google Glass is pretty cool,but…
• Better imaging• Stereo/Light field• HD  UHD• Speed• More sensors• Wireless power?
• Fashion/ubiquity

40

ENVIRONMENT IMAGING• For telepresence, headset
camera is insufficient• Need “environmentcameras”• Lots of privacy concerns• Localizing environment to aclient?

41

API IMPROVEMENTS• Today’s APIs are power
inefficient• Needed:• Hints

Applications

Libraries (API)• State-less rendering• API commands supply statewith action• Frame-less rendering

CompilersOperating System (OS)Hardware• Compositing deferred andon-demand• Hierarchical geometry• Deferred detail42

CLOUD-DEVICE INTEGRATION• SW Challenge:• Making cloud queries easier• Utilizing the parallelism ofthe cloud• Ultimate challenge:• The “network GPU”
• Analogously extend the GPUmodel to network scale• 109 GPUs  1021 FLOPs?
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SUMMARY• Computing has changed ourworld and will continue to doso• Mobile computing, inparticular graphics, is growing
rapidly and becoming
ubiquitous• Tomorrow’s machines:• Ever improving efficiency• Integrated visual UX• Tied to the cloud• Challenges remain to makethis a reality• Exciting prospects…
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