David Atienza at Nano-Tera 2015 by Breadfruit

Tackling the Energy Problem in the Information Age Era David Atienza Embedded System Laboratory (ESL) EPFL, Switzerland

Thematic Nano-Tera presentation: Energy Nano-Tera Annual Meeting 2015, Bern (May 5th, 2015) ÂŠ ESL/EPFL 2015

Brief History of Computing Communication Era Information Age Era 1970sMainframes

1990s

2000s

Today+

PC Era

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

92,000 ops/sec

Intel Xeon, 2011

96,000,000,000 ops/sec © ESL/EPFL 2015

Big Data: Data Collection and Processing Driven by Computing Evolution

4.4ZB

2013

44ZB

2020

[source: Economist]

 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! © 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

[source: Microsoft Research]

Strategically vital to remain being an innovation society © ESL/EPFL 2015

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

Gene Sequencing

Commerce

Weather Prediction Life data

Data centers

Medical Analytics

 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) © ESL/EPFL 2015

Performance Growth in Computing Stopped, Power/Transistor Maintained 10,000,000 Num Transistors (in Thousands)

1,000,000 100,000

Relative Performance Clock Speed (MHz) Power Typ (W) NumCores/Chip

10,000 1,000 100 10 1 0 1985

Same server size, higher power density, much more energy spent 1990

1995 2000 Year of Introduction

2005

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

2010 … 2015

Billion Kilowatt hour/year

Higher Demand + Lower Efficiency: Data Center Energy Not Sustainable As Today! 280 240 200 160 120 80 40 0

Datacenter Electricity Demands In the US

A Modern Data Center

[Energy Star]

[IDC]: Mega DCs 70% total in 2018

17x football stadium, $3 billion

2001

2005 2009 2013 2017 Change energy increase trend, how to “flatten” it?

 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 © ESL/EPFL 2015

Nano-Tera Energy Track: Holistic Energy Management in Information Age Era  Energy generation, supply and storage  Energy-efficient computing and management

Computing Systems: integration and specialization

Computing in Biological Systems: Brains = Efficient and Approximate 1012 ops/J 1) Low energy consumption when idle, ↓ 2) Optimal power and cooling provision, 1pJ/op 3) Only as accurate as really needed ↓ 1GOPS/mW

[Courtesy: Ruch, IBM11] © ESL/EPFL 2015

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

Processing unit controller

Oxidant

IC Package Power delivery Vias

Efficient chip design & energy recovery  Built adaptive multi-core in FD-SOI  59ºC operation, and 6W of free power to power-up the caches

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

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

Infrastructure

Fuel

Microchannels

Software

Liquid power and cooling delivery

Technology (e.g., FDSOI)

Infrastructure (e.g., cooling, power) 12 12

Efficient Data Access in Data Centers

soNUMA fabric

Software

Coherence domain 2

Coherence domain 1

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

ď&#x192; In-memory big data processing with low-cost volume servers

Infrastructure

Direct remote access

Specialized server architectures

System Software (e.g., messaging)

Technology (e.g., FDSOI)

Infrastructure Scalable analytics with bandwidth and latency matching (e.g., cooling, power) fully-integrated high-premium mainframes

ÂŠ ESL/EPFL 2015

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

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

Infrastructure

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

Technology (e.g., FDSOI)

Infrastructure Sub-threshold multi-core Efficient chip design with exact and inexact (e.g., cooling, power) hardware accelerators will be integrated in June 2015

 Multiprocessor with hardware accelerators  Inexact arithmetic, graceful degradation © ESL/EPFL 2015

14 14

Holistic Cooling of Data Centers Software

System Software (e.g., messaging)

Infrastructure

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

Technology (e.g., FDSOI)

Cooling power efficiency 100x better than with air cooling, overall data center energy can be reduced by 50% Serverand rack-level cooling Infrastructure (e.g., cooling, power) modeling and control  Passive cooling  pumping power not required  Better control power saving modes (80% less energy)

15 15

Nano-Tera Energy Track: Holistic Energy Management in Information Age Era  Energy generation, supply and storage  Energy-efficient computing and management

Energy delivery and management: monitor energy reliably and adapt power supply (or store)

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

Smart Grid

 Smart Grid: Real-time monitoring

Smart Sensors

Smart Buildings

 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.

Smart Grid: Installed EPFL Microgrid With Real-Time Monitoring See: smartgrid.epfl.ch, Real time monitoring: http://smartgrid.epfl.ch:443/EPFL_SE.html

Proof of finegrained energy monitoring of large environments

ÂŠ ESL/EPFL 2015

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

uncertain production

compensate with reserves

 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 © 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)

Shine: Reliable and Energy-Efficient

Fuel Production

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

Solar Cell Hydrogen Fuel Fluidics

Shine: 1st Miniaturized Membrane-less Electrolyzer for Sunlight

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

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

Acknowledgements: IcySoc, Synergy, HeatReserves, Smart Grid, CMOSAIC and YINS ÂŠ ESL/EPFL 2015