PowerPoint Presentation

Smart EnergyDavid Atienza, EPFL YINSChristophe Ballif, EPFL SynergyMaher Kayal, EPFL SmartGridJohn Lygeros, ETHZ HeatReservesChristophe Moser, EPFL SHINEChristian Piguet, CSEM IcySoCChair: Mario Paolone, EPFL

David Atienza, EPFL

YINS

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

Prof. David Atienza ESL EPFLProf. Luca Benini IIS ETHZDr. Edouard Bugnion EC EPFLProf. Babak Falsafi PARSA EPFLProf. Lothar Thiele TIK ETHZProf. John Thome LTCM EPFLDr. Fabrice Roudet Eaton Gmbh Marcel Ledergerber Credit SuissePatrick Segu BrainServeNano-Tera Annual Meeting, May 20th 2014 (Lausanne)

3First of all I would like to thank you for giving us the opportunity to present to you today our project EXTREME, which stands for Energy- and Thermal-Aware Design of Many-Core Heterogeneous Datacenters. My name is David Atienza, from EPFL, and I am the PI of the project. Let me start by introducing our consortium, which includes 6 academic partners (four from EPFL and 2 from ETHZ) and three swiss-based industrial partners (Credit Suisse, BrainServe and Eaton). On behalf of EPFL, apart from myself, we have Prof. Babak Falsafi and Dr. Jackson Marinissen, from the Computer Science and Mechanical Eng. Dept. For ETHZ we have Prof. Luca Benini from the Electrical and Microelectronics Engineering Dept, Finally, we have Marcel Ledergerber, VP of IT and Infrastructure Management at Credit Suisse, and Fabrice Roudet, Innovation Manager at the IT Division in Eaton.

Information Technologies (IT) Is Ever More Indispensable But Energy-Efficient IT Needed

Data CentersEra of knowledge economy and Big Data in science50% of economic value in developed countries [Economist]Datacenter are key in supply-chain of products/services

Centralized processing with minimal-energy useComputing paradigm to enable energy-proportional services

Molecular DynamicsGene Sequencing

Financial Simulations

Medical AnalyticsWeather Prediction

This project motivation starts from the widely accepted observation that Information technologies (or IT) are of key importance for the developement of our society.In developed countries, large-scale computing systems or datacenters represent a core pilards for the development of our society, both from the economical and scientific viewpoint.

In order to consolidate IT, we require a computing paradigm that can optimally use energy, in order to provide energy-proportional services. 4

Higher Demand + Lower Efficiency: IT Energy Not Sustainable with Current Datacenters!Modern datacenters increase power demands 20 MWSince 2005 - Limited voltage scaling: Trans. smaller, but similar used powerPower is beginning to clearly dominate costs in datacenter management

In Switzerland, 3-4% of all electricity, growing at >20%Swiss industry is heavily based on services and requires significant IT supportSwiss Energy Strategy 2050: sustainable IT needs to be at its core

Billion Kilowatt hour/year2001 2005 2009 2014 2019

A Modern Datacenter

17x football stadium, $3 billion 50 million Swiss homes

However, since 2005 we have witnessed a increase in power density that has produced growth in IT demand that has translated in a dramatic growth of datacenter electricity demands due to increase of power density, such that today power dominates costs in latest large scale-datacenters (2% of energy world-wide). Furthermore, in societies like Switzerland this growth is even more pronounced as services are key for its main industry (banking, pharmatheutical, food, etc.). Therefore, with the approved Swiss Energy Strategy 2050: 2x reduction in Watts/person by 20505

Why Cant Industry Fix This?IT industry/market is horizontal:Per-vendor layerWell-defined interfacesNear-neighbor optimization at best

Big cloud operators (Google, Amazon, Facebook, etc.)Driving the marketDo cross-layer optimizationsBut,Only optimizing their setupsNot vendors of technologiesAnd hold data and services hostage

Middleware(data, web services)ApplicationRuntime System(scripting, DSLs)Operating System(resource management)Server(processor, mem, storage, network)Infrastructure(cooling, power)

Industry has been pursuing research in this area of energy efficient IT for the last 5-6 years, but the current IT industry market is very much horizontal, and the main vendors focus on specific layers of the complex datacenter ecosystem.Only big cloud operators (Google,etc), who are driving the market can do cross-layer optimizations. But

We need to develop free and scalable IT technologies for datacenter design, and there are many opportunities today (at system- and component-level).6

YINS Approach: System-Level Energy-Efficient IntegrationBalanced and inter-disciplinary consortium to cover datacenter designMulti-scale system engineering: chip to datacenter level (both HW/SW)Synergy of disciplines: electrical eng., mechanical eng. and computer sciencesIndustrial participation: 3 industrial full partners and 3 third-party partners

ESL-EPFLIIS-ETHZEC-EPFLPARSA-EPFLTIK-ETHZLTCM-EPFL

Cloud computing SW images: Duncan et al. CIO, 2009

Barroso & Hlzle 2009: The datacenter as a computer: an introduction to the design of warehouse-scale machines; WSC: warehouse-scale computer

Figure: Distribution of service disruption events by most likely cause at one of Googles mainservices (preliminary data, 6 weeks onlyfrom Googles Robert Stroud).

We have argued that hardware components must undergo significant improvements in energyproportionality to enable more energy-efficient WSC systems. However, more intelligent powermanagement and scheduling software infrastructure does play an important role in this area. Forsome component types, achieving perfect energy-proportional behavior may not be a realizablegoal. Designers will have to implement software strategies for intelligent use of power managementfeatures in existing hardware, using low-overhead inactive or active low-power modes, as well asimplementing power-friendly scheduling of tasks to enhance energy proportionality of hardwaresystems. For example, if the activation penalties in inactive low-power modes can be made smallenough, techniques of the class described in the PowerNap article (Meisner et al. [55]) could be usedto achieve energy-proportional behavior with components that only support inactive low-powermodes.This software layer must overcome two key challenges: encapsulation and performance robustness.Energy-aware mechanisms must be encapsulated in lower-level modules to minimizeexposing additional infrastructure complexity to application developers; WSC application developersalready deal with unprecedented scale and platform-level complexity. In large-scalesystems, completion of an end-user task also tends to depend on large numbers of systems performingat adequate levels. If individual servers begin to exhibit excessive response time variabilityas a result of mechanisms for power management, the potential for service-level impact is fairlyhigh and can result in the service requiring additional machine resources, resulting in little netimprovements.

The inevitability of failures in WSCs makes fault-tolerant software inherently more complex thansoftware that can assume fault-free operation. As much as possible, one should try to implement a fault-tolerant software infrastructure layer that can hide much of this failure complexity fromapplication-level software. Gray [35], looks not at Internet services but instead examines field data from the highly fault-tolerant Tandem servers between 1985 and 1990. He also finds that hardware faults are responsible for a small fraction of total outages (less than 10%). Software faults (~60%) and maintenance/operations faults (~20%) dominate the outage statistics. Oppenheimer et al.[58] studied three Internet services, each consisting of more than 500 servers, and tried to identify themost common sources of service-level failures. They conclude that operator-caused or misconfiguration errors are the largest contributors to service-level failures, with hardware-related faults (server ornetworking) contributing to 1025% of the total failure events.

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~50% less

Significant reduction of overall datacenter energy budget with scalable PUE = ~1.0 for all sizes and utilization levelsYINS: Project Outcomes and Impact Develop open and green datacenter technologies at system levelAdaptive to multiple conditions and industrial needs (e.g., Swiss IT)Critical for the long-term IT independence of Switzerland

PUEUtilization100%1.032.50

1.6540-80%~1.00Homogeneous setupHeterogeneous setups

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Thank You

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Christophe Ballif, EPFL

Synergy

Synergy:Systems for ultra-high performance photovoltaic energy harvestingChristophe BallifPV-Lab, EPFLCSEM, PV-centerNeuchtel

What?12

Systems for ultra-high performance photovoltaic energy harvesting

Why?13Phase-out of nuclear power production within the next decades Limited space available. High efficiency approaches

Highly competitive photovoltaics market

Novel approaches

Electronic devices have become more mobile in recent yearsSpecialty devices

From W to GWThe best PV devices for energy generation (1000 W/m2) are also the best devices for energy scavenging (from 10 to 1000 lumen) J = -Jill +Jo[eqV/kT-1]+V/Rp

Jo small (e.g. high bandgap), Rp high (no leakage current)

High bandgap cells perform better than c-Si in indoor environment (light spectrum and diode law) !

How?15

Source: EmpaTop cell: Narrow-bandgap to harvest visible lightPerovskite cellsGaAs nanowire cellsWide-bandgap Chalcogenide cellsBottom cell: Wide bandgap to harvest near-infrared lightCrystalline/amorphous silicon heterojunction cellsCopper indium gallium selenideTandem solar cells with efficiencies beyond the single-junction limit, i.e. > 30%

Consortium16

Maher Kayal, EPFL

SmartGrid

S3 Grid: Smart grids, Smart buildings and Smart sensors for Optimized and Secure Management of Electricity Distribution using dedicated microelectronic ICs and real time ICT.Lausanne, 20th of May 201414.10.09

S3

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Project Structure and Challenges19.04.1319

Smart GridSmart BuildingsSmart SensorsTransmission NetworkSmart Grid Real time monitoring:Real time power system state estimation and emulation.ICTs dedicated layer.Smart BuildingsControl:Demand side management, using intelligent plugs (eSmart) to create a cluster of controllable loads (power distribution).Smart SensorsLocal power optimization respecting building occupancy: Zero-power sensors network.Intelligent "human" management of the building by using granular monitoring systems.

Active Distribution Network Control20

TimeReference

MonitoringControl

StateEstimationalgorithm

Protections

DataConcentrationTelecomNetworkPMUActive DistributionNetwork(ADN)Advanced synchrophasor estimation algorithms for the deployment of PMUs in ADNs.

Resilient PMU synchronization methods for ADNs.Data concentration, time alignment and bad data detection algorithms.Applications for the real-time control and protection of ADNs (e.g., fault location and identification, voltage control, etc.)

PMU calibratorPMUSynthesizer

KF1KF2KF3

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Active Distribution Network State Estimation21

DataConcentrationMonitoringControlTimeReference

StateEstimationalgorithmTelecomNetwork

ProtectionsPMU

accuratefast (sub-second)reliableReal-Time State Estimation for ADNsActive DistributionNetwork(ADN)in every network condition(normal and emergency)Recursive SEKalman FilterADNstateNetwork topologyMeasurementsProcess modelPredictionTimeUpdateEstimationMeasurementUpdate

True StateMeasurementsKalman Filter

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Real Time IP based Communication for Active Distribution NetworkDedicated infrastructure.First network deployment to support real time state estimation of several medium voltage buses. Cyber-security architecture.12.11.1222

19.04.13

Smart building management23

Monitoring Acting (ON/OFF)Communication

Inertial Loads

Specific Loads

Battery poweredLoads

Smart Lights

Presence radio sensor

Temperatureradio sensor

Real-TimemonitoringAppliancesSmart Control

John Lygeros, ETHZ

HeatReserves

Automatic Control Laboratory, ETH Zrich

www.control.ethz.ch

HeatReserves: Project OverviewJohn Lygeros

Building Energy ManagementMotivation:Standards: room temperature, air quality, in a given range with predened probability

Cost of energy, green energy

Slow building dynamics.

Uncertain weather forecastsObjectives:Increase energy eciency

Maintain user comfort

ComputationWeather forecast Occupancy forecast

Energy eciency User comfort

Building

Europe : 40% energy used in buildings

Use weather forecast for planning, take into account forecast uncertainty and minimize energy while respecting comfort constraints

Buildings for grid ancillary servicesTraditional electricity grid

Inexible, uncertain demand

Flexible, deterministic supply

Feedback control of supply

Increased in renewables

Inexible, uncertain supply

Who can pick up the slack?

Flexible, controllable demand?

Feedback control of demand through price/other signals

Buildings for grid ancillary services

Potential of thermal storage for ancillary servicesFew large or many small buildingsExploit to manipulate electricity demandSell slack to grid as reservesFacilitate introduction of renewables

Interplay of engineering issues Building climate control for ancillary servicesPopulation control of small consumers,Communication requirements

and economic issuesGrid value of ancillary services vs. consumer loss of welfare

Marketing schemes for consumer participation

Other issues: Privacy,

Team

Power Systems (ETH-PSL)Control Systems (ETH-ACL)Grid Operations (SwissGrid)Building Management(EMPA)Economics (HSG-IWOE)

Research questions Four questions investigatedWhat is the economic value of additional ancillary services to the Swiss grid?How can HVAC systems of oce buildings be controlled to provide ancillary services?

How can a large number of household appliances be controlled to provide ancillary services?How can consumers be induced to take part in such demand response schemes?

Plus validation on the NEST building (EMPA)

Christophe Moser, EPFL

SHINE

The Shine project, which stands for Solar Hydrogen integrated nano electrolysis, looks at the engineering challenge of generating fuel, such as hydrogen from sunlight. We concentrate on a photovoltaic system to generate a current and then an electrolyzer to generate hydrogen by splitting water.

The solar energy is stored in hydrogen which can be transported or used onsite at a later time.

Specifically, we are taking an engineering approach to find design rules to make cost effective solar hydrogen generators and then do the research to implement these rules.

When the electrolyzer gets smaller, the electrical resistance increases thus more voltage is required to push the same current though it, hence the load curve shifts to the right.

There is an optimal value for F. Values above Fopt results in high costs from mainly the catalyst (more surface) and below Fopt, the low production rate (low efficiency) makes more costly.32

Optimizing Solar-Hydrogen Production

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LargeelectrolyzerSmallelectrolyzerOptimalelectrolyzer

The left graph indicates the cost of hydrogen for different catalyst combinations when the area of photon absorption (PV) is equal to the area of Electrolysis. (Called integrated as both the photon absorber and water splitting occur at the same location). In this case the catalyst is the cost driver.

The right graph is when the area is optimized for cost. It shows that the catalyst is no more the main cost, but the PV is !

Then an obvious next step to drive cost down is the minimize the PV area by concentrating sunlight.

Drive research into novel ways of concentrating sunlight with moderate concentration (20x typically). So that is what we are doing. We came up with a way of concentration sunlight using a planar concentrator that does not require an external mechanical sun tracking system.

Next-generation solar concentrators

Solar Driven Water Splitting

A practical problem when you concentrate sunlight onto a PV is heat management. (80% of the energy is dissipated as heat !).

So cooling the PV is important. We are looking at cooling it with the feedstock itself (water).

Left is an implementation with a 3-junction cells made by CSEM on the cathode side of which a platinum layer is deposited. The assembly is then immersed into a liquid electrolyte which acts as the coolant and the medium that generate hydrogen. (integrated approach)

On the right, the same cell without the platinum layer is wired to a membrane electrode assembly. The cooling is done by moving the fluid around the PV before going into the electrode assembly.35

Unified SHINE Modeling Platform Electromagnetic Wave Propagation (EM)Semiconductor Charge Transport (SC)Heat Transfer(HT) Reacting Fluid Flow(RFF)Fluid Flow(FF)Electrolyzer Charge Transport(EC)

Finally, a multi-physics model is being implemented that takes into account the heat, fluid flow to estimate the hydrogen production. This is a refined complete model compared to the one used in the first slide to estimate cost.36

for tomorrow.

In summary, our engineering model approach to make practical hydrogen generators gives us design guidelines that drives research and implementation:

Results are:

Optimized value for the ratio of photon detection area to electrolysis area. PV is the main cost driver which lead us to develop novel sun concentration systemsHeat management using the fluid feedstock (microfluidics)Wired approach (electrical wire from PV to electrolyzer)37

Christian Piguet, CSEM Andreas P. Burg, EPFL

IcySoC

The IcySoC ProjectA Smart, Ultra-Low-Power Platform for NanoTera Applications

IcySOCRTD 2013

IcySOCRTD 2013The Need for Energy Efficient Electronics

NanoTera applications require significant intelligence (computing power)

Cloud

YesterdayTodayTomorrow(autonomous)

IcySOCRTD 2013

IcySOCRTD 2013The Energy Efficiency Challenge

1012ops/J 1pJ/op 1GOPS/mWUse high energy whenneed to work hard

Low energy when doing little is good enough

Challenge: Energy Proportionality[RuchIBM11]

IcySOCRTD 2013

IcySOCRTD 2013The Mission of IcySoCProvide new solutions and a universal platform for energy-efficient computing for Wearable Health MonitoringEnvironmental MonitoringThe Internet of ThingsPersonal Electronics and Entertainment

IcySOCRTD 2013

IcySOCRTD 2013The IcySoC Platform

Many-core architecture organized in clustersLarge number of simple, but efficient processor coresEnergy-efficient application-specific accelerators

PULP: parallel ultra-low-power platform

IcySOCRTD 2013

IcySOCRTD 2013Why is Low-Power Design Difficult?

IcySOCRTD 2013

IcySOCRTD 2013Cellphone turns offGo beyond this point by offering another dimension of scalabiltity that ensures graceful performance degradation

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Approximate Computing: A New ParadigmThe Economist, June 2nd-8th 2012Oh, thats near enough: Letting microchips make a few mistakes here and there could make them much faster and more energy-efficient

Noisy real world Inputs

PerceptualLimitationsApproximatelySparseNo single Golden ResultMultimediaCommunications

Iterative Self healingPattern Recognition

ApplicationResilienceWeb search

IcySOCRTD 2013

IcySOCRTD 2013Allow some graceful QoS degradation for the benefit of power consumption and energy efficiency

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Smart EnergyDavid Atienza, EPFL YINSChristophe Ballif, EPFL SynergyMaher Kayal, EPFL SmartGridJohn Lygeros, ETHZ HeatReservesChristophe Moser, EPFL SHINEChristian Piguet, CSEM IcySoCChair: Mario Paolone, EPFL

IcySOCRTD 2013

IcySOCRTD 2013