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
Het
erog
eneo
us C
ompu
ting
Nex
t ge
nera
tion
com
putin
g
Cro
ss c
ompo
nent
opt
imiz
atio
n
Cos
t-ef
fect
ive
softw
are
Arc
hite
ctur
e fo
r D
ata
Del
uge
Rel
iabl
e ub
iqui
tous
sys
tem
s
Loca
lity
& c
omm
unic
atio
n
Conclusion
Exiting new opportunities are ahead of us!