presentazione astday 2018 roveri pdf · 2018. 2. 28. · 1k x – x gpus, neural&hw...

Internet  of  Smart  Thingswhere machine learning meets embedded systems

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Politecnico di Milano

Prof.  Manuel  RoveriDipartimento  di  Elettronica,  Informazione  e  Bioingegneria  

Politecnico  di  Milano  

“Stand  on  the  shoulders  of  Giants”

Alan Turing (1912-1954)Claude  Shannon (1916-2001)

Computer  science  and  AI:  the  rise  of  both  disciplines

Computer  science  and  AI:  the  rise  of  both  disciplines

Claude  Shannon (1916-2001)

A

B

C

“A  Symbolic  Analysis  of  Relay  and  Switching  Circuits”,  Master's  degree  thesis,  Massachusetts  Institute  of  Technology,  1937

Computer  science  and  AI:  the  rise  of  both  disciplines

Claude  Shannon (1916-2001)

A=“Fever”

B=“Flu”

C=“Night”

“A  Symbolic  Analysis  of  Relay  and  Switching  Circuits”,  Master's  degree  thesis,  Massachusetts  Institute  of  Technology,  1937

“ill”

“Stay  at  home”

TURING  TEST

Computer  science  and  AI:  the  rise  of  both  disciplines

Alan Turing (1912-1954)

Garry  Kasparov  (2012)  playing  Turing’s  Chess  (1948)

A  test  for  a  machine  to  be  called  "intelligent”

TURING  TEST

Computer  science  and  AI:  the  rise  of  both  disciplines

Alan Turing (1912-1954)

Garry  Kasparov  (2012)  playing  Turing’s  Chess  (1948)

A  test  for  a  machine  to  be  called  "intelligent”

Generations  and  seasons

1945

Eniac

Univac

1960 1970 1980 Today

Transistor Integrated  Circuits

Microprocessors

StatisitcalMethods

Pioneering  works  in  the  field  of  AI

Bayesian  Inference

Neural  Networks

K  Nearest  Neighbour

SVMDeep  Learning

BackProp

Generations  and  seasons:  the  evolution   of  computation  and  memory  

1945

Eniac

Univac

1960 1970 1980 Today

Transistor Integrated  Circuits

Microprocessors

StatisitcalMethods

Pioneering  works  in  the  field  of  AI

Bayesian  Inference

Neural  Networks

K  Nearest  Neighbour

SVMDeep  Learning

BackProp

Computational  PowerAvailable  Memory

Computational  needsMemory  Requirements

20x  – 50x 100x  – 1000x

50x  –100x

1Kx  –10Kx

GPU,  FPGANeural  HW

SupercomputersHPC

RAM

ComputationSpeedup

PC

Embedded  PCs

Embedded  Systems

0.0001x  – 0.0005x

0.1x  –0.5x

0.01x–0.05x

AI  and  Technology

20x  – 50x 100x  – 1000x

50x  –100x

1Kx  –10Kx

GPUs,Neural  HW

Supercomputers(Systems  of  GPUSs  or  Neural  HWs)

RAM

ComputationSpeedup

Embedded  PCs

Embedded  Systems

0.0001x  – 0.0005x

0.1x  –0.5x

0.01x–0.05x

AI  and  Technology

Intelligent Cyber-Physical Systems

Deep Learning

GPU,  FPGANeural  HW

SupercomputersHPC

PC

Intelligent  Internet-­of-­Things  and  Cyber-­Physical  SystemsCyber

Domain

Physical  

Domain

Self-­awareness

Self-­Diagnosis

Reliability

Self-­healing

Adaptation

Intelligent  Processing  

ofPhysical  Sensing

Cognitive  Mechanisms  for  Actuation  and  Control

Intelligent  IoT and  Cyber-­Physical  Systems

POLIMI  and  STMicroelectronics:Designing   Intelligent  Cyber-­Physical  Systems

The  Intelligent   Embedded  Sensors:  learning  Recurrent  Neural  Networks  (ESNs)

Trained  on  a  Coordinatorof

the  CPS

Trained  on  the  Intelligent  Embedded  Sensor

The  Intelligent   Embedded  Sensors:  robustness  mechanisms  to  shield  removal

Self-­‐ability  to  manage  the  removal  and  the  insertion  of  the  sensor  shield  board   from  the  STM32  main  board

The  Coordinator:  dependency-­graph   learning  and  network  management

The  Server:  data  analysis,  processing  and  visualization

http://131.175.156.3:5984/stdashboard/_design/ST-­DASHBOARD/main2.html

The  Testbed:   Intelligent  Monitoring  of  Datacenters

What  about  Deep  Learning?  

20x  – 50x 100x  – 1000x

50x  –100x

1Kx  –10Kx

GPUs,Neural  HW

Supercomputers(Systems  of  GPUSs  or  Neural  HWs)

RAM

ComputationSpeedup

PC

Embedded  PC

Embedded  Systems

0.05x  – 0.1x0.0001x  – 0.0005x

0.1x  –0.5x

0.01x–0.05x

Deep Learning

Deep Learning

What  about  Deep  Learning?  

20x  – 50x 100x  – 1000x

50x  –100x

1Kx  –10Kx

RAM

ComputationSpeedup

PC

Embedded  Systems

0.05x  – 0.1x0.0001x  – 0.0005x

0.1x  –0.5x

0.01x–0.05x

Embedded  Systems

How  to  meet  Embedded  Systems  with  Deep  Learning?

20x  – 50x 100x  – 1000x

50x  –100x

1Kx  –10Kx

RAM

ComputationSpeedup

PC

Embedded  Systems

Approximate  Computing

0.05x  – 0.1x0.0001x  – 0.0005x

0.1x  –0.5x

0.01x–0.05x

Embedded  System  Code  Optimization

Re-­design  of  the  CNN  architecture

Progetto Regione Lombardia -­ Smart  Living  (2018-­2019)

Sistema Intelligente per  il M onitoraggio e  la  Predizione della Solidità Strutturale di  edifici e  infrastrutture e  per  la  pianificazione dell’intervento -­ SIMPSS  

Thank  you  for  the  attention!