Embed Size (px)
Citation preview
Internet of Smart Thingswhere machine learning meets embedded systems
Adv
ance
d Re
sear
ch Intelligent Embedded Systems
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!
