Big Data SSGJanuary 24, 2012
Big Data: Thoughts from the perspective of the semiconductor industry
Celia Merzbacher, SRC VP for Innovative Partnerships
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80Gb cost $9,000,000 !!!
in 1982 dollars
1982: Best available storage technology was the IBM 3350
iPod(5G) 80GB
2012
126 IBM 3350’s = storage in
1 iPod
80Gb cost <$100
in 2012 dollars
Each unit: 635 MB $70,000
Hardware Advances Enable Big Data
In 1982…Aug 17—the first compact disc goes on sale
7
Oct 1—Sony launches the first compact disc player
US semiconductor market share was dropping…
Federal funding for academic research on silicon was declining…
The pipeline of talent was drying up.
Federal $
Research
Talent
Market Share
Semiconductor Market Share
0 %
20 %
40 %
60 %
80 %
US
Japan
In the early 1980’s
In 1982…
11Robert Noyce
Jack KilbyErich Bloch
The Semiconductor Research Corporation (SRC) was launched with the support of visionary industry leaders.
Objectives:Define relevant research directions Explore potentially important new technologies Generate a pool of experienced faculty &
relevantly educated students STAY ON MOORE’S LAW
Moore’s Law: 1971-2011
1970 1975 1980 1985 1990 1995 2000 2005 2010 20151,000
10,000
100,000
1,000,000
10,000,000
100,000,000
1,000,000,000
10,000,000,000
R² = 0.94986565616323
Year
MPU
tran
sisto
rs
Cu in
terc
onne
cts
Hig
h-K
gate
insu
l.Pb
-fre
e pa
ckag
ing
FinF
ET
Triple Core
Dual Core
Quad CoreHex Core
Eight Core
2
ln 22.13
0.3252Y years
1E+08 1E+15 1E+221E+04
1E+10
R² = NaN
, /b bit s
, m
IPS
(In
stru
ctio
ns
pe
r se
c-o
nd
)
Benchmark capability m (IPS) as a function of b (bit/s)
trN f
Power is the main issue for further scaling of high-performance computing
~100 W
Scaling up performance
IBM Watson supercomputer The most recent and most impressive
demonstration of an artificial intelligence computer system Capable of answering questions posed in natural
language Winner of the Jeopardy! quiz show in 2011
~3000 processor cores (POWER7) each consisting of 1.2B transistors and operating at
3.5GHz approximate total binary throughput ~ 1022 bit/s.
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& power
~200kW of power
EPA estimated data centers used 1.5% of total US electricity in 2007
1E+08 1E+15 1E+221E+04
1E+10
R² = NaN
, /b bit s
, m
IPS
(In
stru
ctio
ns
pe
r se
c-o
nd
)
Benchmark capability m (IPS) as a function of b (bit/s)
1014 IPS1019 bit/s30 W
Basic algorithms need to work in very few steps! (L.G Valiant, A quantitative theory of neural computation, Biol. Cybern. (2006) 95
~100 W
Estimates of computational power of human brain:
Binary information throughput:
b ~1019 bit/sGitt W, “information - the 3rd fundamental quantity”, Siemens Review 56 (6): 36-41 1989(Estimate made from the analysis of the control function of brain: language, deliberate movements, information-controlled functions of the organs, hormone system etc.
Number of instruction per second
m ~ 108 MIPSH. Moravec, “When will computer hardware match the human brain?” J. Evolution and Technol. 1998. Vol. 1 (Estimate made from the analysis brain image processing)
How can we decrease the energy needed to move/store data?What can we learn about information processing from Nature?
1000x algorithmic efficiency
Infrastructuralcore
The IT Platform of Today:Mobiles at the Edge of the Cloud
[J. Rabaey, ASPDAC’08]
MobileAccess
The Cloud
Mobile data growth[Source: Cisco VNI Mobile, 2011]
Mobile traffic grew 2.6x in 2010 (nearly tripling for 3rd year)Driven by Tablets
The SwarmInfrastructuralcore
The Swarm at The Edge of the Cloud
[J. Rabaey, ASPDAC’08]
MobileAccess & Relay
The Cloud
New STARnet* Center: TerraSwarmDirector: Ed Lee, UC-Berkeley
*STARnet is a subsidiary of SRCwww.src.org/program/starnet/tsrc/
Si-mCell: A hypothetical 1-mm3 Si computer
Memory: 40 kbitLogic: 320 bit
1mm
Logic MemoryFmin (nm) 4.5 10
N 320 40,000Ebit (J/bit) 3×10-18 ~10-15 (read)
Ecycle (J) ~10-15 ~10-13 (read)fclk, MHz 100Pactive (W) 10-7 10-5
Pleak (W) 6.4×10-7 assumed lowPtotal (W) ~1.1×10-5
Qactive (W/cm2) 2 170
Qleak (W/cm2) 11 assumed lowQtotal (W/cm2) ~200
Memory access is the most severe limiting factor of Si-Cell due to line charging
Exceeds capability of known cooling techniques
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Nanoelectronics Research InitiativeFinding the Next Switch
UC Los AngelesUC BerkeleyUC IrvineUC RiversideUC Santa Barbara
Notre Dame PurduePenn State UT-Dallas
UT-Austin RiceUT-Dallas NCSUU. Maryland Texas A&MGIT
SUNY-Albany
Purdue U. VirginiaHarvard GITColumbia MIT
Over 40 Universities in 19 States
(co-funds all centers)
Virginia Nanoelectronics Center (ViNC) University of Virginia Old Dominion University College of William & Mary
BrownU. AlabamaNorthwestern
Columbia Carnegie Mellon Illinois-UC MITStanford Notre Dame (2)Nebraska-Lincoln Columbia / U. FloridaPenn State U. of MinnesotaPrinceton / UT-Austin Cornell / PrincetonUC-Santa Barbara Drexel University / UI-UC / U. PennUC-Riverside / GeorgiaVirginia Commonwealth / UC-R / Michigan / U. Virginia UC-Riverside / UC-I / UC-SD / Rochester / SUNY-BuffaloU. Pittsburgh / U. Wisconsin-Madison / Northwestern
NRI Nanoelectronic Devices
HeterojunctionsNotre Dame, Penn State
NanowiresPenn State
GrapheneNotre Dame
Tunnel DevicesMIND
Spin-Wave DeviceWIN - UCLA, UCSB
Spin-FETWIN - UCLA
Nanomagnet LogicMIND - Notre Dame
WIN - Berkeley
Spin-Torque DeviceWIN - UCI
PtMn
Co70Fe30
RuCo40Fe40B20
MgOCo60Fe20B20
12oH
+Idc
-100 0 100 2003
4
5
6
7
8
R (
k)
Direct current ( A)
T = 100 KH = 1.4 kOe
0 1 2 3 4 5
-0.015
-0.010
-0.005
0.000
T = 100 KH = 1.4 kOeIac
= 5 A
Idc
= 0 A
Vd
c (m
V)
Frequency (GHz)0 1 2 3 4 5
0
2
4
6
8T = 100 KH = 1.4 kOeIac
= 5 A
Idc
= 100 A
Vd
c (m
V)
Frequency (GHz)
A B
C D
Bilayer pseudoSpinSWAN - UT Austin
All-Spin Logic INDEX - Purdue U.
Graphene PN Junction Device
INDEX - SUNY Albany
A
F
U = ‘1’
B C
(a)
y
x
z
‘0’U =
VGnVn
VGp
Vp
Graphene Integration
INDEX – SUNY Albany
Graphene
Tunneling Insulator
Graphene
FMD
Graphene
Substrate
Contacts Insulators Oxidation
Graphene Processes SWAN – UT Dallas
Device and Architecture Benchmarking MIND/WIN/INDEX/SWAN – Led by K. Bernstein, IBM
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Breakthrough Technology Challenges for next decades
From fundamental physics it seems likely that the scaling of current technology will end in the few nanometer regime. NRI is working to develop replacement technologies So far, a replacement technology has not been found
Are there other models for information processing technologies that offer the promise to sustain Moore’s Law?
Looking to organic systems, i.e., at the intersection of chemistry, biology, and information processing
ACBSSpecifications of a Human Cell
- 10 mm overall size- - 0.36 nm between base pairs in DNA. Average protein is 5 nm.
- 107 biochemical operations per second- 1 pWatts power consumption- 30,000 node gene-protein molecular network with nanoscale
devices.- 20 kT per molecular operation
(vs. 104–105 kT in advanced nanoelectronics)- Functions: sensing, communication, actuation, feedback
regulation, molecular synthesis & transport, detoxification, defense, self assembles from a single embryonic cell.
Biology computes efficiently and precisely with noisy and unreliable components on noisy real-world signals.
Courtesy of Rahul Sarpeshkar, Analog Circuits and Biological Systems Group, MIT
Memory
Nature Has Been Processing Information for a Billion Years
Logic
L
L
L
L
L
L
L
L
LL
LL
L
M (DNA)
S
L
L
L
L
L
E
E
E
E
C C
E E
Si-mCell
V=1mm3
Bio-mCell – A Living Cell
Our studies show that the Si-mCell cannot match the Bio-µCell in the density of memory and logic elements, nor operational speed, nor operational energy:
Memory: 1000x moreLogic: >10x morePower: 1,000,000x lessAlgorithmic efficiency: 1000x more
About 500 of these cells would fit in the cross-section of a human hair
25
DNA-inspired memory DNA volumetric memory density far exceeds (1000x) projected
ultimate electronic memory densities Potential for very low-energy memory access Goal: Demonstrate a miniaturized, on-chip integrated DNA
storage
HardDiskDrive NAND flash DRAM DNA in cellRead/Write latency 3-5 ms/bit ~100ms/bit <10 ns/bit <100ms/bitEndurance (cycles) unlimited 104-105 unlimited unlimited
Retention >10 years ~10 years 64 ms >10 yearsON power (W/GB) ~0.04 ~0.01-0.04 0.4
Aerial Density ~ 1011 bit/cm2 ~ 1010 bit/cm2 ~ 109 bit/cm2 n/a Volumetric Density n/a 1016 bit/cm3 ~1013 bit/cm3
<10-11
1019 bit/cm3
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DNA: The Ultimate Hard Drive?
http://www.wired.com/wiredscience/2012/08/dna-data-storage/
DNA Memory
Researchers stored an entire genetics textbook in less than a picogram of DNA — one trillionth of a gram — an advance that could revolutionize our ability to save data.
5.27×106 bit
DNA memory can be stable 100+ years
European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SD, UKAgilent Technologies, Genomics–LSSU, 5301 Stevens Creek Boulevard, Santa Clara, California 95051, USA
Encoded into DNA code computer files totaling 739 kilobytes of hard-disk storage and with an estimated 5.2 × 106 bits
Synthesized and sequenced the DNA, and reconstructed the original files with 100% accuracy.
Storage scheme is theoretically scalable beyond current global information volumes
Current trends in DNA synthesis costs should make the scheme cost-effective for sub-50-year archiving within a decade.
Possible New SRC Initiative: SemiSynBio
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Organizing Committee: Rahul Sarpeshkar/MITTimothy Lu/MITSami Issa / ATICAndrew Hessel / AutodeskEric Klavins / U. WashingtonLarry Sumney / SRCSteven Hillenius / SRCRalph Cavin / SRCVictor Zhirnov / SRC
Exploring potential benefits to the semiconductor industry arising from synthetic biology
Meeting Date: February 22&23, 2013Meeting Place: Cambridge, MA
Take Away Messages
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Success of Big Data will depend on continued advances in computation hardware, aka semiconductors
Moore’s Law (for CMOS) is facing physical limits Power is the main issue for further scaling of high-
performance computing There are no evident replacement technologies
Nanoelectronics research is seeking new devices New research turns to biology
DNA-based memory Using/mimicking Nature in other areas may allow
Moore’s Law (for performance) to continue “beyond CMOS”.
Industry—through SRC—continues to fund leading edge university research in partnership with Government
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SRC Creates Value Through Partnerships
• Maximizes technological progress• Leverages investments• Utilizes the strengths of each sector • Expands and replenishes the
professional community
Tactical Perspective, “Can-Do” Attitude,
FUNDING
Industry
Strategic Perspective,National Needs,
Credibility, FUNDING
Government Universities
Creativity, Faculty Expertise, Student Resources
Semiconductor Research Corporation: A Family of Distinct, Related Program Entities
Updated January 2013
Each entity has a distinct set of member companies and Government partners.For more information go to www.src.org
Global Research
Collaboration
Ensuring vitality of current industry
Focus Center Research Program Phase VI
STARnet
Early research engagement of
key long horizon
semiconductor challenges
Energy Research Initiative
Emphasis on efficient/clean
energy generation, storage and distribution
Education Alliance
Attracting and educating the
next generation of innovators
and technology leaders
Nanoelectronics Research Initiative
Beyond CMOS –the next switch and associated architectures
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Benefits of SRC Approach (to Univ & Govt)
Value of research is enhanced Provide insight on industry needs to researcher community Input and feedback from industry at periodic reviews E-seminars and e-workshops facilitate near real-time sharing of
research results and tech transfer Interactions and opportunities for personnel exchanges among
universities and industry
Student education is enhanced Industry liaisons & mentors engage with students Participation in TECHCON, SRC’s annual technical conference at
which students present research and network with industry representatives.
Opportunities for student internships at SRC member companies.
Explore new research directions E.g., joint workshops, new program “spin offs”, etc.
SRC created an industry-guided global university research ecosystem
Since 1982… Over $1.6B invested by SRC
participants 9,195 students 2,025 faculty members 261 universities in 27 countries
1500 students 500 faculty 120 universities worldwide
In 2012…20X
increase over 1982
Essential SRC Features
Industry-driven, consensus-based goals embodied in:• Moore’s Law• ITRS (International Technology Roadmap for Semiconductors)
Focus on pre-competitive university research (>5 yr time horizon) Members have rights to resulting IP Involves the current industry experts (provide input/ feedback/
oversight and tech transfer) Managed by an independent entity (facilitates interactions among
members and with universities & government agencies) Nimble and adaptable (~1/3 of projects turn over annually) Accountable; value-driven; efficient; effective Attracts world-class researchers (faculty & students)
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