New HiPEAC Roadmaps · 2013. 6. 17. · Part of the HiPEAC roadmap is required reading for AVDARK...

M. DURANTON, D. BLACK-SHAFFER, S. YEHIA, K. DE BOSSCHERE

http://www.hipeac.net/roadmap

2

2009 20112008

http://www.hipeac.net/roadmap(these slides are a short version of the HiPEAC presentation)

HiPEAC Roadmaps

Part of the HiPEAC roadmap is required reading for AVDARK

You will find it and other required reading in the ”Extra course papers” directory.

As specified in the ”Reading instructions”: page 2-33 are required reading page 34-40 read-through (RT)

Impact on

Society

???7 HiPEACResearch objectives

HiPEACStrengths

???

HiPEACWeaknesses

Efficiency Complexity

Dependability

Technological constraints

???

Technological opportunities

Big data meets energy in an intelligent connected

physical world

Application pull

Business trends

Trends influencing Computing Systems

Application Pull BusinessTrends

• Data Deluge• Intelligent Processing• Ubiquitous

Communication

• Convergence• Specialization• Post-PC Devices

5

Data Deluge

6

Growth of data storage in Exabytes

7

Intelligent processing of “natural” data

Source: “The Landscape of Parallel Computing Research: A View from Berkeley”Krste Asanovic et all.

More and more applications are not only “number crunching”Recognition, Mining, Synthesis

Posture: Lying Down

8

Implicit and natural computing

Ubiquitous computing in a connected world

Courtesy Jan M. Rabaey, UC Berkeley, updated for this HiPEAC vision

Sensory swarm, actuators and real world data

InfrastructureCore (cloud)

Mobileaccess

9

Smart housecities, …

Trends influencing Computing Systems

Application Pull BusinessTrends

• Data Deluge• Intelligent Processing• Ubiquitous

Communication

• Convergence/standards• Specialization• Post-PC Devices

10

Convergence

Broadcast

Telecom

11MacBook image © Jared C. BenedictPhone, TV images © LG Electronics

IP, Internet•Business models• Standard(s)• Interoperability• …

Post-PC devices

Ubiquitous access

Personalized services

Delocalized computing and data

Massive data processing systems • Cloud• Peer to peer• Personalized

12iPad image © Apple, IncMP3 player image © J A S P E R@flickriPhone image © K!T@flickr

PC Market

Computing Systems: Drivers

14

Big data meets energy

in an intelligent connected

physical world

Application pull

Business trends

Efficiency Complexity

Dependability

Technological constraints

???

Technological opportunities

Big data meets energy

in an intelligent connected

physical world

Technology push

Application pull

Business trends

Technological trends influencing Computing Systems

Constraints Opportunities• Frequency Limits• Power Limits• Dark Silicon

• CMOS Phonotic• Non-volatile memories• 3D Stacking• New paradigms

16

Technological constraintsWe are at a turning point

Dark silicon

Continuation of Moore’s Law Power limits

17

Moore’s law: increase in transistor density

Data from Kunle Olukotun, Lance Hammond, Herb Sutter, Burton Smith, Chris Batten, and Krste Asanovic

18

Limited frequency increase more cores

19

Data from Kunle Olukotun, Lance Hammond, Herb Sutter, Burton Smith, Chris Batten, and Krste Asanovic

Limitation by power density and dissipation

2009: GP CPU = 130 W (45 nm)2009: Consumer SoC = 10W2009: Mobile SoC = 1 W

20

Data from Kunle Olukotun, Lance Hammond, Herb Sutter, Burton Smith, Chris Batten, and Krste Asanovic

Dark Silicon

21

Source: Krisztián Flautner “From niche to mainstream:can critical systems make the transition?”

Specialization leads to more efficiency

Source: Bill Dally, « To ExaScale and Beyond »

www.nvidia.com/content/PDF/sc_2010/theater/Dally_SC10.pdf 22

In 22 nm, swapping 1 bit in a transistor has an energy cost:

~ 1 attojoule (10-18 J)

Moving a 1-bit data on the silicon cost:

~1 picojoule/mm (10-12 j/mm)

Moving a data 109 per second (1 GHz) in silicon has a cost:

1 pJ/mm x 109 s-1 = ~1 milliwatt/mm

64 bit bus @ 1 GHz: ~64 milliwatts/mm (with 100% activity)

For 1 cm of 64 bit bus @ 1 GHz : 0,64 W/cm

On modern chips, there are about several km of wires on chip, even

with low toggle rate, this leads to several Watt/cm2

23

Locality and communications management

Technological consequences

Efficiency localityFrequency limit

parallelismEnergy efficiency

specialization

Ease of programming

24

Technological trends influencing Computing Systems

Constraints Opportunities• Frequency Limits• Power Limits• Dark Silicon

• CMOS Phonotic• Non-volatile memories• 3D Stacking• New paradigms

25

Optical interconnectsCMOS photonic is the integration of a photonic layer with an electronic

circuit.Advantages of CMOS photonic are:

Use of standard tools and foundry, wafer scale co-integration Lower energy (~100 fJ/bit), (wire: ~1 pJ/mm) High bandwidth (10 Gbps), Low latency (~10 ps/mm)

Silicon modulator

SOI wave guideI

2D networkInversed taper

Germanium photdetector

LASER

Source: CEA, Ahmed Jerraya26

Example: Memristive Devices Principle

1 L. Chua and S. Kang, Proceedings of the IEEE, 19762 D. Strukov et al., Nature, 2008

Metal (Mx+1 layer)

Metal (Mx layer)

Insulator• Oxide• Solid electrolytic• Organic material

ElectrodesMIM

V

VdRdt

R

Vth

-Vth’

Nonlinear characteristic

iixRv ).,(= ),( ixfdtdx =

Crossbar(University of Michigan)

Source: CEA, C. Gamrat

Non-volatile memories….

27

3D stacking

Multiple integration with 3D stacking…Source: STMicroelectronics & CEA 28

Technology also drives us to think differently…

Technology

System

Prog. Model

• Stochastic computing• Biologically inspired computing• Organic Computing• Autonomous computing, Self-*

• Smart spaces (smart house, town, building, rooms,…)

• Intelligent dust (smart sensors)

• 3D stacking• Photonic interconnect• Non-volatile memories• Molecular computing• More-than-Moore• Spintronics• Chemical computing• Biologically inspired cells• Memristors• ...• Also silicon based!

29

Efficiency Complexity

Dependability

Technological constraints

???

Technological opportunities

Big data meets energy

in an intelligent connected

physical world

Technology push

Application pull

Business trends

Core Computing Systems Challenges

Improving efficiency Multiple performance metrics Power defines performance Communication defines performance Heterogeneity and accelerators to the

rescue

Managing complexity The reign of legacy code Parallelism seems to be too complex for

humans Hardware complexity

(4G is 500x more complex than 2G)

Improving dependability Worst case design is not an option

anymore Systems must be built from unreliable

components Safety and security!

Impact on

Society

???7 HiPEACResearch objectives

HiPEACStrengths

???

HiPEACWeaknesses

Efficiency Complexity

Dependability

Technological constraints

???

Technological opportunities

Big data meets energy in an intelligent connected

physical world

Application pull

Business trends

Impact on

Society

???7 HiPEACResearch objectives

HiPEACStrengths

???

HiPEACWeaknesses

Efficiency Complexity

Dependability

Technological constraints

???

Technological opportunities

Big data meets energy in an intelligent connected

physical world

Application pull

Business trends

Derived HiPEAC Research ObjectivesC

ompu

ting

syst

ems

System complexity

• Heterogeneous computing systems

• Locality and communications management

• Cost-effective software for heterogeneous multicores

• Cross-component/cross-layer optimization for design integration

• Next-generation processor cores

• Architectures for the Data Deluge• Reliable systems for Ubiquitous

Computing37

Frequency limit parallelism

Energy efficiency heterogeneity

Ease of programming

38

Cost-effective software for heterogeneous multicores

Detailed HiPEAC Research areas

39

40

Global optimization

Technology and new devices

Efficiency System Complexity Dependability

Data DelugeEnergy Wall, Connected, Real world dataTurning point for Moore’s law

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Conclusion

Exiting new opportunities are ahead of us!