Michel_Digitale_Innovationen_Umwelt_Gesundheit_Handout

IBM Research - Zurich | Science & Technology
© 2016 IBM Corporation
Digitale Innovationen für Umwelt und Gesundheit
Bruno Michel, Mgr. Smart System Integration, IBM Research – Zurich, Switzerland
Member US National Academy of Engineering
Member IBM Academy of Technology

IBM Research – Zurich – Digitale Innovationen für Umwelt und Gesundheit
Politics Triggered Energy and Efficiency Research at IBM
Bruno Michel, bmi@zurich.ibm.com2

Mega Trends with Implications
Big Data
Cognitive Computing
The End of
Transistor Scaling
Datacenter
Carbon Footprint
Dense and Efficient
Systems Roadmap
Wearables and
Healthcare
The Internet
of Things IoT
Renewables Energy
Cost and Climate

Agenda
 Zero Emission Datacenters – from Idea to largest European Computer in 5 Years
 Concentrated Solar and Heat Pumps – Make Sustainable Energy Competitive
– Reduce losses to unusable heat
– Accelerated progress in IT and Sustainable Energy
 Dense Small Computer Biological Concepts Transform IT Industry
– Brain inspired packaging
– New Roadmap to replace Moore’s law
– Microserver as key step
 Human centric Sensing and Computing
– Mainframe computing – PCs – mobile computing – wearable computing
– Cognitive companion and the augmented human
 Information Technology Transforms Healthcare
– Wearable home care - Elderly care - Watson health

CMOS 80ºC
Water In 60ºC
Micro-channel
liquid coolers
Heat
exchanger
Direct „Waste“-Heat use e.g. heating
Biological inspired:
Vascular systems
optimized for low
pressure transport
Water Out 65ºC
Water
Pump
Biological Concepts Transform IT Industry
Hot-Water-Cooled Zero Emission Datacenters „Aquasar“

Zero-Emission Data Centers
 High-performance chip-level cooling improves
energy efficiency AND reduces carbon footprint
– Cool chip with DT = 20ºC instead of 75ºC
– Save chiller energy: Cool with T > 60ºC hot water
– Re-use: Heat 700 homes with 10 MW datacenter
 Need carbon footprint reduction
– EU, IPCC, Stern report targets
– Chillers use ~50% of datacenter energy
– Space heating ~30% of carbon footprint
 Zero-emission concept
valuable in all climates
– Cold and moderate climates:
energy savings and energy re-use
– Hot climates: Free cooling, desalination
 Europe: 5000 district heating systems
– Distribute 6% of total thermal demand
– Thermal energy from datacenters absorbed

Power Dissipation
up to 3.6 MW
HPLinpack Performance
2.9 PFLOPS
IDPX DWC dx360 M4
9288
Power Dissipation
up to 1.3 MW
HPLinpack Performance
2.8 PFLOPS
NXS DWC nx360 M5
3096
Power Usage Effectiveness
PUE 1.1
World’s
Most Powerful &
Energy Efficient
x86 Supercomputers
Phase I (2012)
SuperMUC at Leibniz Rechenzentrum
Phase II (2015)
 9288 IBM System x iDataPlex dx360 M4
– 43997256 Components
– 8.08 m2 CMOS 4.22x1013 transistors
– 74304 Samsung 4 GB DIMMs
 11868 IB Fibre Cables 192640 m
 Cooling
– 34153 m Copper
– 18978 Quick Connects
– 7.9 m3 Water
 Mass 194100 kg
 IBM System x / Lenovo NeXtScale
DWC nx360 M5
Bruno Michel, bmi@zurich.ibm.com
Cool MUC Phase III (2016)
7

Sustainable generation
Electricity and heat
Fresh water scarcity
Renewable heating and cooling
High performance
microchannel
coolers
Growth in
Cloud and Big
Data
Heat exchanger
Pump
Underfloor
heating
Economic value of heat reduces datacenter total
cost of ownership by 50-70% lower energy cost
60°C
65°C
>700 W/cm2
Zero-emission
datacenter
High-concentration
PV/thermal
Adsorption
heat pump
Membrane distillation
desalination
Electrochemical redox
energy conversion
Experimental Smarter Energy Research Agenda

Development of Most Efficient Solar Technology
Stage 1: Prototype (2015)
 4.3 m2 area
 18 dish-mirrors (vacuum-actuated)
Stage 2: Early Adopter (2016)
 40.0 m2 area
 36 dish-mirrors (vacuum-
actuated)
Stage 3: Final Product
 40.0 m2 area
 36 dish-mirrors (vacuum-actuated)
 Sun tracking (2 axis)

Transport dynamicsAdsorbents
Refrigerants
Silica gel Zeolite
Activated
carbon
Alumino-
phosphates
MOFs Salt composites
Improve
adsorpti
on
capacity
Improve
dynamic
utilization
Select for
application
Water, Methanol, Ammonia,
Sulfur Dioxide, Carbon Dioxide
Heat Driven Heat Pumps or Adsorption Chillers

BIG DATA … is Exploding from Disparate Sources
DataVolumeinExabytes
Multiple sources: IDC,Cisco
70
60
50
40
30
20
10
AggregateUncertainty%
5000
4000
3000
2000
1000
0
2005 2010 2015
Data quality solutions for a
fraction of the enterprise data.
Number of networked devices is 2x the
population. All sensor data has uncertainty.
Social media account # exceeds population.
Highly uncertain in expression and content.
* Truthfulness,
accuracy or
precision,
correctness
Volume Velocity Veracity*Variety
Data at Rest
Tera- to exabytes of
existing data to
process
Data in Motion
Streaming data,
milliseconds to
seconds to respond
Data Variety
Structured,
unstructured, text,
multimedia
Data in Doubt
Data inconsistency
ambiguities, latency,
deception, approx.

Cognitive Computing … a New Era of Computing
1900 1950 2011
Tabulation Programmatic Cognitive
enabling new opportunities
and outcomes
Discovery
Probabilistic
Big Data
Natural language
Intelligent options
Punch cards
Time card readers
Electronic computation
Search
Deterministic
Enterprise data
Machine language
Simple outputs
Workload Optimized
Systems
Healthcare Finance
Cognitive computing with
learning algorithms
Jeopardy

Relative
1
The End of Scaling
Since 2006/07: Circuits still become smaller and cheaper, BUT not faster and not more efficient.
Since 2015: Circuits still become smaller but not cheaper  Moore’s economic “law” is dead
2007 2007
Performanc
e / efficiency
scaling
stops
Power limits
performance
Single
thread
performance
stagnates
Classic Silicon-on-Insulator FinFET NWFET
H. Fahad et al., 2012
2007: Dennard
scaling stopped
Wiring “Crisis”
since 2007
2007

(R)evolution of Information Technology
Improve efficiency
through density
• Device centric viewpoint (left)
 Device performance dominates
– Power depends on better device performance
• Density centric viewpoint (below)
 Communication efficiency dominates
– Power and memory proximity depend on size
– Dominant for large systems (>Peta-scale)
CMOS replaced
Bipolar due to its
higher density!
• Density and efficiency on log-log line
– Brain is 104 times denser AND 104 times more efficient
• Independent of switch technology
– No jumps mechanical – tube – bipolar – CMOS – neuron
• Communication as main bottleneck
– Memory proximity lost in current computers (1300 clock access)
– Detrimental for efficiency
Evolution
Revolution

Brain: synapse network
Multi-Chip Design
System on Chip
3D Integration
Global wire length reduction
Heat removal limit constrains electrical design
Paradigm Change: Vertical Integration
Through silicon via
electrical bonding
and water insulation
scheme
• A large fraction of energy in computers is
spent for data transport
• Shrinking computers saves energy

Allometric Scaling in Biology
 Increasing system size in biology: Scaling behavior: R ∞ Ma
 Most famous: Metabolic rate with increasing organism size
 Kleiber (1932): Scaling exponent is different
→ R ∞M3/4 and not R ∞M2/3
 West (1997): Exponent ¾ explained by hierarchical
branched supply and drain network G. West et. al.:
The fourth Dimension
of Life: Fractal Geometry and
Allometric Scaling of Organisms,
Science 284, 1677 (1999).
~30x
Red dotted line
Exponent a = 1
Biology teaches how to build dense
and efficient complex systems
Branched networks are as important as genetic code!
• From Engineering to Nature
 From Nature to Engineering
– Lung (blood in+out, air in/out)
– Kidney (blood in+out, water
out)
– Tree (roots, leaves)
– River basins and drying mud
• Human built
– Dwellings and Cities
– Computers

Scaling to 1 PFlops in 10 Liters
System with
1 PFlops in
10 liters
P. Ruch, T. Brunschwiler, W. Escher, S. Paredes, and B.
Michel, “Towards 5 dimensional scaling: How density
improves efficiency in future computers”, IBM J. Res.
Develop. 55 (5, Centennial Issue), 15:1-15:13 (2011).
 Efficiency comparison
– 1PFlops system currently consumes ~10MW
– 0.1 PF ultra-dense system consumes 20 W
 Ultra-dense Bionic System
– Stack ~10 layers of memory on logic
– Stack several memory-logic stacks to stack of stacks
– Combine several blocks of stacks to MCM (MBM)
– Combine MCMs to high density 3D system
 Key enabling technologies
– Interlayer cooling
– Electrochemical chip power supply
 Impact
– 5’000x smaller power
– 50’000’000x denser
– Scalability to zetascale
With cooling
With cooling

Target system
• T4240 1.8GHz node
• 12 cores 2
threads
• 48GB of DRAM
• 220 GFLOP
• 2U rack unit
• 128 nodes
• 3072 Threads
• 6 TB DRAM
• 28 TFLOP
Micro Server or Datacenter in a Box
Leading cooling and power delivery
concept for Dome project
 2x P5020 compute nodes
 2x P5040 compute nodes
 1x power converter + 1x storage module
Compute nodes
Active cooled heat sinkHeat spreader
SoC
Backplane
Power delivery
Increase density using hot water
cooling structure for power delivery
 Density: Key differentiator
1000x denser and
10x more efficient

Brain Inspired Computing
Complicated
Fast
Reliable
Software
Fault Sensitive
Power hungry
Complex
Slow ?
Unreliable ?
Learning
Fault Tolerant
Power efficient
• Stepwise introduction of brain inspired concepts: Form – Function – Material
• Step 1 (Form): Brain inspired packaging with classical CMOS  Now
• Step 2 (Function): Brain-inspired, non-von Neumann architecture  Ongoing
• Step 3 (Material): DNA computing …  Later
Computer
vs.
Human in
Chess and
Jeopardy

Wearables enable a
highly efficient dense
computing.
Latest technology wave
Each generation with 10x
larger $ market, 100x
higher efficiency and
density, and 100x lower
cost.
Symbiotic wearables
21

Historical Evolution of Wearable Capabilities
DigitalAnalog Multi-Functional Multi-Functional
Connected

Wearable Technology
 Wearables: Devices with central processing capability, sensors, and connectivity
 What will make wearables more essential?
– Wearables that help turn in real time data into insights, decisions and actions
Picture: Credit Suisse Bruno Michel, bmi@zurich.ibm.com23
 Augmented Senses: Visual, Audio,
Tactile, chemical, electrical
 4 challenges: energy sparsity, dynamic
conditions, need for basic functionality,
and security/privacy.
 Novel weararable integrated
sensing/actuating
 Solve the 4 challenges of wearables

What Technologies are involved?
 Sensors
 Storage/Computation
 Communication
 Interface
 Energy and Efficiency
 Packaging technology

Hearables – All in One Devices
 Augmented interaction using emotional awareness.
– Hands free and invisible
– Multi function, smart and cognitive
 Human - Human vocal communication;
 Seamless communication and symbiotic human computer interaction
 Hospital of the Future
 Cognitive Companion for the Elderly
 Fitness, Wellness, Chronic
Disease Monitoring
认为 Think
Denke
Tror
考えますDink
Earplug in movie “Her”
Annapurna Pictures
Real-time translation

26

Mobile
Social
Cloud
Analytics
Watson and Me
Understands and engages me
Learns and improves over time
Helps me discover
Has endless capacity for insight
Reasons
Watson searches and analyses data,
returning evidence-based
recommendations.
Understands
Watson reads and understands
documents and data - both
structured and unstructured - at
massive scale.
Learns
Decisions being made by leading
subject matter experts feed the
engine
27

Inquiry
Decomposition
Answer
Scoring
Models
Responses with
Confidence
Inquiry
Evidence
Sources
Models
Models
Models
Models
ModelsPrimary
Search
Candidate
Answer
Generation
Hypothesis
Generation
Hypothesis and Evidence
Scoring
Final Confidence
Merging & Ranking
Synthesis
Answer
Sources
Inquiry/Topic
Analysis
Evidence
Retrieval
Deep
Evidence
Scoring
Learned Models
help combine and weigh the
Evidence
Hypothesis
Generation
Hypothesis and Evidence
Scoring
How Watson works: DeepQA Architecture
1000’s of
Pieces of Evidence
Multiple
Interpretations
of a question
100,000’s Scores from many
Deep Analysis Algorithms
100’s sources
100’s Possible
Answers
Balance
& Combine
28

Application Focus
Hospital of the Future
 Get average patient out
of hospital one day early
Cognitive Companion
for the Elderly
 Allow elderly to live/stay
longer at home
Fitness, Wellness, and
Chronic Disease Monitoring
 Healthcare cost reduction
and improved quality of life
Picture: Medtronic

Value: Reduce Stay at Hospital
How can home care be improved to cover the majority of today’s hospital «cases»?
Today: Very few patients
qualify for home-care.
With cognitive technologies: Majority
of patients treated under home-care.

Internet of the Body
Hospital of the Future: At home healthcare and monitoring using
wearables, cognitive hypervisor hub, and IBM Bluemix PaaS
Cognitive HyperVisor
Internet of the Body

Outlook
 Natural cognitive companion with emotional awareness
 Support for improved living and recovery
 Online translation and audio person identification
 Seamless communication and symbiotic human computer
interaction
认为 Think
Denke
Tror
考えますDink
Earplug in movie “Her”
Annapurna Pictures
Watson Health
32

Summary
 Zero Emission Datacenters – from Idea to largest European Computer in 5 Years
 Concentrated Solar and Heat Pumps – Make Sustainable Energy Competitive
– Reduce losses to unusable heat
– Accelerated progress in IT and Sustainable Energy
 Dense Small Computer Biological Concepts Transform IT Industry
– Brain inspired packaging
– New Roadmap to replace Moore’s law
– Microserver as key step
 Human centric Sensing and Computing
– Mainframe computing – PCs – mobile computing – wearable computing
– Cognitive companion and the augmented human
 Information Technology Transforms Healthcare
– Wearable home care - Elderly care - Watson health

Thank you for your Attention
 Acknowledgement
 Arvind Sridhar, Frank Libsch, Hyung-Min Lee, Ismael Faro, John Knickerbocker, Jonas
Weiss, Jonathan E Proesel, Kang-Wook Lee, Keiji Matsumoto, Mehmet Soyuer, Minhua
Lu, Mounir Meghelli, Norma E Sosa cortes, Paul S Andry, Rahel Straessle, Roy Yu,
Sebastian Gerke, Shriya Kumar, Sufi Zafar, Teodor K Todorov, Theodore G van Kessel,
Thomas Brunschwiler, Yuksel Temiz, Yves Martin
 Ingmar Meijer, Stephan Paredes, Patrick Ruch, Severin Zimmermann
 Emmanuel Delamarche, Govind Kaigala, Robert Lovchik, Ute Drechsler, Onur Goecke,
Aditya Kashyap, Stefan Harrer, Ben Mashford

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