FUTURE (AND PRESENT) TECHNOLOGIES...| 5 LOOKING BACK…1 COMPUTER PER HOUSE EFECS - Electronic Components and Systems l Marc Duranton l Brussels, December 7th, 2017 The Altair 8800

FUTURE

(AND PRESENT)

TECHNOLOGIES

Marc DurantonCEA Fellow

Commissariat à l’énergie

atomique

et aux énergies alternatives

EFECS - Electronic Components and Systems

Brussels, December 7th, 2017

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“The best way to predict the future is to invent it.”

Alan Kay

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LOOKING BACK… 1 COMPUTER FOR THE WHOLE PLANET

EFECS - Electronic Components and Systems l Marc Duranton l Brussels, December 7th, 2017

ENIAC (Electronic Numerical Integrator And Computer),

built between 1943 and 1945.

ENIAC contained 20,000 vacuum tubes,

7200 crystal diodes, 1500 relays. It weighed more than 27 t,

was roughly 2.4 m × 0.9 m × 30 m in size, occupied 167 m2

and consumed 150 kW of electricity.

EDVAC was delivered in 1949.Functionally,

EDVAC was a binary serial computer with

automatic addition, subtraction, multiplication,

programmed division and automatic checking

with an ultrasonic serial memory capacity of

1,000 44-bit words. EDVAC's average addition

time was 864 microseconds and its average

multiplication time was 2,900 microseconds.From Wikipedia

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LOOKING BACK… 1 COMPUTER PER (MAJOR) COUNTRY


"I think there is a world market

for maybe five computers."

Thomas Watson, president of IBM, 1943

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LOOKING BACK… 1 COMPUTER PER HOUSE


The Altair 8800 by MITS – 1974 - Intel 8080 CPU.

"1024 word" memory board populated with 256 bytes.

The BASIC language was announced in July 1975

and it required one or two 4096 word memory boards

"There is no reason anyone would

want a computer in their home." Ken Olsen, founder of Digital Equipment Corporation, 1977

IBM PC – 1981- Intel 8088 CPU.

Basic configuration 16K RAM.

From Wikipedia

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LOOKING BACK… 1 COMPUTER SMARTPHONE PER PERSON


iPhone, introduced

June 29, 2007;

Samsung 32-bit RISC

ARM

Underclocked to

412 MHz

128 MB eDRAM

Storage

4, 8 or 16 GB flash

memory

From Wikipedia

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IMPACT OF COMPUTING ON SOCIETY

Remember: the iPhone was introduced just 10 years ago…

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LOOKING FORWARD… HER (THE MOVIE)


Multiple computers closely

linked (or implanted?) with

the individual through a

single “entity”.

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Entering in

Human and machine collaboration erathe centaur era


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THE CENTAUR ERA…

In Advanced Chess, a "Centaur" is a man/machine team.

Advanced Chess (sometimes called cyborg chess or

centaur chess) was first introduced by grandmaster Garry

Kasparov,

with the objective of a human player and a computer chess

program playing as a team against other such pairs.

(from Wikipedia)

More recently, Ke Jie (human world champion in the

“Go” game), after being defeated by AlphaGo on May

27th 2017, will work with Deepmind to make a tool

from AlphaGo to further help Go players to enhance

their game.


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• Artificial Intelligence is changing the man-machine interaction –

natural interfaces, ”intelligent” behavior

• Image and situation understanding

• Voice recognition and synthesis

• Unstructured data pattern recognition, direct interfacing with the world

• Creating the bridge between cyber and real world: enabling true CPS

• …decision taking…

• The new systems should make intelligent and trustable decisions

ENABLED BY ARTIFICIAL INTELLIGENCE

(AND DEEP LEARNING)


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KEY INGREDIENTS FOR TRUSTABLE SYSTEMS

Security

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IS DEEP LEARNING WORKING SO WELL?

OR NOT….

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- Non natural images or noise

⇒ train the network to recognize fakes

- Problem of bad (incomplete) specifications

⇒ Create a learning data set including ”extra” inputs

But it is and will remain a problem (like bugs in software)

SECURITY AND DEEP LEARNING

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KEY INGREDIENTS FOR TRUSTABLE SYSTEMS

Mixed-criticality

Security Privacy

Safety

Compatibility with existing system

Transparency

?

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New

services

Smart sensors

“Dumb” Internet of

Things devices

Big Data

Data Analytics / Cognitive computing

Cloud / HPC

Cyber Physical Systems

Enabling Intelligent data

processing at the edge:Fog computing

Edge computing

Stream analytics

Fast data…

• Secure exchanges between the edge devices and the cloud

• With human in the loop: Centaur era

ENABLING EDGE INTELLIGENCE

Intelligent edge

devicesTransforming

Data into

Information

as soon as

possible

True collaboration

between edge devices

and the cloud ensuring:

- Data security / Privacy

- Lower bandwidth

- Better use of cloud

C2PS: COGNITIVE ( CYBERNETIC* AND PHYSICAL ) SYSTEMS

* As defined by Norbert Wiener: how humans, animals and machines control and communicate with each other.


Computing should be

where are the data

(avoid moving data!)

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System should be autonomous to

make good decisions in all conditions

Safety will impose that

basic autonomous

functions

should not rely on “always

connected” or “always

available”

EMBEDDED INTELLIGENCE NEEDS LOCAL HIGH-END COMPUTING


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Dumb sensors Smart sensors: Streaming and

distributed data analytics

Bandwidth (and cost) will require more local processing



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Dumb sensors Smart sensors: Streaming and

distributed data analytics

Bandwidth (and cost) will require more local processing


Fog computing And if you need a response

in less than 1ms, the server

has to be in less than 150 Km

( the speed of light is

299 792 458 m/s )


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Privacy will impose that some intelligent processing

should be done locally and not be sent to the cloud.

Example: detecting

elderly people falling in

their home



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The Hype cycle - 2017


• Deep Learning

• Machine learning

• Autonomous vehicles

• Virtual assistants

• Smart robots

• Edge computing

• IoT platforms

• Connected home

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"As soon as it works, no one calls it AI anymore"

John McCarthy

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ELEMENTS OF ARTIFICIAL INTELLIGENCE

Rule-based

AI

Analysis of

“big data”

ML-based AI:

Deep

Learning*

* Reinforcement Learning, One-shot Learning,

Generative adversarial networks, etc…

From Greg. S. Corrado, Google brain team co-founder:

– “Traditional AI systems are programmed to be clever

– Modern ML-based AI systems learn to be clever.

Left brain

Right brain

From imperative programming to

Declarative programming, statistical programming, …

Telling the machine What to do, not anymore How

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NEW JOB: TEACHER FOR AI


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ALPHAGO ZERO: SELF-PLAYING TO LEARN

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Cognitive solutions for complex

computing systems:

• Using AI techniques for

computing systems

• Creating new hardware

• Generating code

• Optimizing systems

• Similar to Generative design

for mechanical engineering

Managing complexity


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USING AI FOR MAKING CPS SYSTEMS:

“GENERATIVE DESIGN” APPROACH

Motorcycle swingarm: the piece that hinges the rear wheel to the bike’s frame

The user only states desired goals and constraints

-> The complexity wall might prevent explaining the solution

“Autodesk”

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“Neural Architecture Search”, using a

recurrent neural network to compose

neural network architectures using

reinforcement learning on CIFAR-10

(character recognition)

2017: GOOGLE; USING DEEP LEARNING

TO DESIGN DEEP LEARNING

From arXiv:1611.01578v2, Barret Zoph, Quoc V. Le

Google Brain


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• Ne-XVP project – Follow-up of

the TriMedia VLIW

(https://en.wikipedia.org/wiki/Ne

-XVP )

• 1,105,747,200 heterogeneous

multicores in the design space

• 2 millions years to evaluate all

design points

• AI inspired techniques allowed

to reduce the induction time to

only few days

=> x16 performance increase

EXAMPLE: DESIGN SPACE EXPLORATION FOR

DESIGN MULTI-CORE PROCESSORS1 (2010)

1 M. Duranton et all., “Rapid Technology-Aware Design Space Exploration for Embedded HeterogeneousMultiprocessors” in Processor and

System-on-Chip Simulation, Ed. R. Leupers, 2010


https://en.wikipedia.org/wiki/Ne-XVP

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Source: 2017 Accenture CMT Digital Consumer Survey

AI AND CONSUMERS

(IN THE USA)


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“Autodesk”

STRATEGIC IMPORTANCE OF AISTRATEGIC IMPORTANCE OF AI

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WIENER

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2017: GOOGLE’S CUSTOMIZED HARDWARE…

… required to increase energy efficiency

with accuracy adapted to the use (e.g. float 16)

(Adequate computing)

Google’s TPU2 : 11.5 petaflops16 of machine learning number crunching

(and guessing about 400+ KW…, 100+ GFlops16/W)

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INCREASED COMPLEXITY AND COST

The initial product designs will need to

generate high revenues to provide good

buyback from the design and yield ramp-up

costs.

• Barrier for specialization to computing

• Barrier for advanced feature

monolithic dies

=> 2.5 stacking with chiplets and

interposers might help solving these…Source IBS, Aug. 2014

28nm 20nm 16nm 10nm 7nm 5nm

$38M $67M$132M

$273M

$593M

$1348M

IC Design Cost

NRE ++

Wafer Cost

16nm 10nm 7nm 5nm

$9885

$11881

$14707

$19620

IC Design and Yield Ramp-up Costs

28nm 20nm 16nm 10nm 7nm 5nm

$59M $91M$176M

$373M

$876M

$2243M


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22FD

28nm

14nm

10nm

7nm

5nm

Next Gen

FinFET

Non planar / trigate / stacked Nanowires

25nm TBOX

20nm LG ISPD SiCRSD

Si channel

2017

2018

25nm TBOX

20nm LG ISPD SiCRSD

Si channel

12FD

Silicon Quantum bits

FDSOI

Technology evolution

Also ETSOI: Extremely-thin silicon on insulator

Vertical TFET, CNFET: Carbon Nanotubes FET,

…


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22FD

28nm

14nm

10nm

7nm

5nm

Next Gen

Mechanical switches

Hyb

rid

lo

gic

Steep slope devices

Si Quantum bits

Disruptive scaling

Monolithic 3D for 3D VLSI

FinFET

Alternative to scaling and diversification

25nm TBOX

20nm LG ISPD SiCRSD

Si channel

2017

2018

25nm TBOX

20nm LG ISPD SiCRSD

Si channel

12FD

Silicon Quantum bits

FDSOI

Non planar / trigate / stacked Nanowires

Also ETSOI: Extremely-thin silicon on insulator

Vertical TFET, CNFET: Carbon Nanotubes FET,

…

Technology evolution


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WHAT WILL BE THE NEXT TECHNOLOGY?


And after IC?

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QUANTUM COMPUTING HARDWARE PROGRESSES

IBM, 2017

Intel Delivers 17-Qubit Superconducting

Chip with Advanced Packaging to QuTech

-> 49 Qubits

From Christian GamratEFECS - Electronic Components and Systems l Marc Duranton l Brussels, December 7th, 2017

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QC System Hardware architecture

• From work by QuTech @TU-Delft

POSSIBLE QUANTUM COMPUTER ARCHITECTURE

“Fu et al - 2016 - A Heterogeneous Quantum Computer Architecture.pdf.” .

QC Software stack

From Christian Gamrat

Quantum computer need conventional

computers


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SYNTHETIC BIOLOGY


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SOME IDEAS COMING FROM THE HIPEAC VISION 2017

42

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CONCLUSION: WE LIVE AN EXCITING TIME!


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Centre de Grenoble

17 rue des Martyrs

38054 Grenoble Cedex

Centre de Saclay

Nano-Innov PC 172

91191 Gif sur Yvette Cedex

Marc Duranton

Marc [email protected]

mailto:[email protected]

Documents

FUTURE (AND PRESENT) TECHNOLOGIES...| 5 LOOKING BACK…1 COMPUTER PER HOUSE EFECS - Electronic Components and Systems l Marc Duranton l Brussels, December 7th, 2017 The Altair 8800