Prof. David Atienza at Nano-Tera 2015

© ESL/EPFL 2015

Tackling the Energy Problem in the Information Age Era

David AtienzaEmbedded System Laboratory (ESL)

EPFL, Switzerland

Nano-Tera Annual Meeting 2015, Bern (May 5th, 2015)

Thematic Nano-Tera presentation: Energy

© ESL/EPFL 2015

Brief History of Computing

From pure computing-intensive centric to data-centric Information Age Era: connected and ubiquitous access

1970s-

PC Era

Communication Era

Mainframes1990s 2000s Today+

Information Age Era

2

© ESL/EPFL 2015

Information Age Enabled by Five Decades of Exponential Growth

Doubling the number of transistors in the same surface every ~24 months since 1965

Intel 4004, 1971

Intel Xeon, 2011

92,000 ops/sec

96,000,000,000 ops/sec

3

© ESL/EPFL 2015

Big Data: Data Collection and Processing Driven by Computing Evolution

Data growth (by 2015) = 100x in ten years [IDC 2012]• Population growth = 10% in ten years

Monetizing data for commerce, health, science or services Big Data is shaping society… Use in whatever we do!

[source: Economist]

4

2013 2020

4.4ZB 44ZB

© ESL/EPFL 2015

Data Analytics is Also Shaping Science Evolution

Science entering “4th paradigm” Analytics using computing

systems on• Instrument data• Simulation data• Sensor data• Human data • …

Complements theory, empirical science and simulation

Strategically vital to remain being an innovation society

5

[source: Microsoft Research]

© ESL/EPFL 2015

Data Centers are Key in the Information Age Era to Keep Up with Data

Data centers

Data center consists of thousands of computing servers• Store, process, and serve user data on behalf of billions

Era of “knowledge economy” and Big Data in science• 50% of economic value in developed countries [Economist]

New computing paradigm to transform data into value (at minimal cost)

Commerce

Gene Sequencing

Financial Simulations

Medical Analytics

Weather Prediction

Life data

6

© ESL/EPFL 2015Source: National Research Council (NRC) – CSTB.org

7

Performance Growth in Computing Stopped, Power/Transistor Maintained

0

1

10

100

1,000

10,000

100,000

1,000,000

10,000,000

1985 1990 1995 2000 2005 2010Year of Introduction

Num Transistors (in Thousands)Relative PerformanceClock Speed (MHz)Power Typ (W)NumCores/Chip

… 2015

Same server size, higher power density,

much more energy spent

© ESL/EPFL 2015

Higher Demand + Lower Efficiency: Data Center Energy Not Sustainable As Today!

Data centers increase power demands 15-20 kW per rack, 20-25 MW DC• With a 3-year replacement policy the energy cost is as high as servers’ investment• Power is beginning to clearly dominate costs in data center Management

In Switzerland, 3-4% of all electricity, growing at >20%• Swiss industry is heavily based on services and requires significant IT support

Bill

ion

Kilo

wat

t hou

r/yea

r

2001 2005 2009 2013 2017 0

4080

120160200240280

Datacenter Electricity Demands In the US [Energy Star]

[IDC]: Mega DCs 70% total in 2018

Change energy increase trend, how to “flatten” it?

A Modern Data Center

17x football stadium, $3 billion

8

© ESL/EPFL 2015

Nano-Tera Energy Track: Holistic EnergyManagement in Information Age Era

Energy generation, supply and storage Energy-efficient computing and management

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© ESL/EPFL 2015



10

Computing Systems: integration and specialization

© ESL/EPFL 2015

Computing in Biological Systems: Brains = Efficient and Approximate

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[Courtesy: Ruch, IBM11]

1012 ops/J↓

1pJ/op↓

1GOPS/mW

1) Low energy consumption when idle, 2) Optimal power and cooling provision, 3) Only as accurate as really needed

© ESL/EPFL 2015 1

Infr

astr

uctu

re

Efficient chip design & energy recovery

Built adaptive multi-core in FD-SOI 59ºC operation, and 6W of free power to

power-up the caches

Liquid power and cooling delivery

Processing unit controller

Fuel

Oxidant

Microchannels

Power delivery Vias

IC Package

Software

1212

System Software(e.g., apps, messaging)

Server Hardware(e.g., CPU, memory, network)

Technology(e.g., FDSOI)

Infrastructure(e.g., cooling, power)

Energy- and Thermal-Aware Design of Many-Core Heterogeneous Datacenters

© ESL/EPFL 2015

Efficient Data Access in Data Centers

Infr

astr

uctu

reSpecialized server architectures

In-memory big data processing withlow-cost volume servers

Software

System Software(e.g., messaging)




soNUMAfabric

Coherence domain 1

Coherence domain 2

Direct remote access

Scalable analytics with bandwidth and latency matchingfully-integrated high-premium mainframes

© ESL/EPFL 2015 14

Infr

astr

uctu

re

Efficient chip design

Multiprocessor with hardware accelerators Inexact arithmetic, graceful degradation

Software

1414

System Software(e.g., apps, messaging)




IcySoC: Inexact Sub- and Near-Threshold Systems for Ultra-Low Power Devices

Sub-threshold multi-core with exact and inexact hardware accelerators will be integrated in June 2015

© ESL/EPFL 2015

Holistic Cooling of Data Centers

Infr

astr

uctu

re

Server- and rack-level cooling modeling and control

Passive cooling pumping power not required Better control power saving modes (80% less energy)

Software

1515

System Software(e.g., messaging)




Cooling power efficiency 100x better than with air cooling, overall data center energy can be reduced by 50%

© ESL/EPFL 2015



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Energy delivery and management: monitor energy reliably

and adapt power supply (or store)

© ESL/EPFL 2015

Smart Grid: New Technologies for Real-Time Monitoring and Grid Management

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Smart Grid

Smart Buildings

Smart SensorsTransmission Network

Smart Grid: Real-time monitoring Real time power system state estimation and emulation. ICTs dedicated layer.

Smart Buildings: Control and demand side management Intelligent plugs (eSmart), cluster of controllable loads (power distribution).

Smart Sensors: Local power optimization (building occupancy) Zero-power sensors network. Intelligent "human" management of buildings.

© ESL/EPFL 2015

Smart Grid: Installed EPFL Microgrid With Real-Time Monitoring

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http://smartgrid.epfl.ch:443/EPFL_SE.htmlSee: smartgrid.epfl.ch, Real time monitoring:

Proof of fine-grained energy

monitoring of largeenvironments

© ESL/EPFL 2015

HeatReserves: Use Thermal Loads of Buildings as Reserves to Integrate Renewables

Use of renewable sources generate forecast errors• Threatening grid reliability

Idea: use thermal loads for additional primary services• Reduces transmission line loads, consumption peak, • Improve service market use

uncertainproduction

compensate with reserves

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© ESL/EPFL 2015

HeatReserves: Use Thermal Loads of Buildings as Reserves to Integrate Renewables

Developing demand-response schemes• Offices: heating, ventilation, air conditioning (HVAC) systems• Houses: Thermostatically Controlled Loads (TCLs)

NEST Experimental Platform in January 2016

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© ESL/EPFL 2015

Solar Cell

Fluidics

Water Splitting

Sunlight

Hydrogen Fuel

Light Concentrator

Shine: Reliable and Energy-Efficient Fuel Production

Only use sun and wáter: reliable hydrogen bateries Multiple disciplines: microelectronics, materials, fluidics, etc.

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© ESL/EPFL 2015

Shine: 1st Miniaturized Membrane-less Electrolyzer for

Sunlight

O2 H2

Proof-of-concept system built… And on theway to “save the sun” (reliably!)

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© ESL/EPFL 2015

Conclusion

Information age is here• Computing systems everywhere• Big Data is becoming common in our society

Nano-Tera Energy Track = smart energy for the future• Energy-efficient computing and energy management• Effective energy generation, provisioning and storage

Great results already achieved by Swiss researchers• 6 Nano-Tera RTD projects, and 1 strategic action working in

key aspects of complete energy ecosystem• Significant participation of industry

But lots to do: look forward to NT Annual Meeting ’16!

Prof. David Atienza at Nano-Tera 2015

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