Un panorama de quelques uns des derniers résultats d’Inria et des méthodes et technologies

1. Research activities of Inria
in energy, green IT &
sustainable development
Jacques Sainte-Marie
Deputy director for science of Inria
&
Team-project ANGE
StationF - September 2019

2. - J. Sainte-Marie
Science at Inria
2
Neuro-physio evaluation of [3D] interaction
Cognitive ease of use
appeal
enjoyment
…
Expected results:
New (objective) metrics
DATA SCIENCE &
KNOWLEDGE ENGINEERING
MODELING &
SIMULATION
OPTIMIZATION
& CONTROL
ARCHITECTURE,
SYSTEMS &
NETWORKS
SECURITY &
CONFIDENTIALITY
INTERACTION &
MULTIMEDIA
ARTIFICIAL INTELLIGENCE
& AUTONOMOUS SYSTEMS
ALGORITHMS &
PROGRAMMING

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People and teams @ Inria
4,500 staff
1800
scientists
2700
Inria
900
staff
1800
Partners
1700
scientists
100
staff
600
permanent
700
permanent
Inria project
teams in 2017
in collaboration
with partners
• 20-30 people per project-team including 5-7
permanent positions
• around a shared research project
• lifetime in [4, 12] years
• Partners: Universities, CNRS, Inserm, INRA
Saclay
Rennes
Bordeaux
Lille
Paris
Nancy
Grenoble
Sophia
155184

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Inria project-team
4
100
appui
20 to 30 people, under the direction of a scientific leader
A precise research theme
An international evaluation at creation and every 4 years
An average duration of 8 years and maximum of 12 years
Well-defined goals
and a shared or common work program
In connection and collaboration with industrial
and scientific partners in France and around the world
Financial and scientific autonomy
A high expectation of transfer and impact
184INRIA PROJECT
TEAMS IN 2017
155IN
COLLABORATION
AN ADDITIONAL ORGANIZATION TO
THAT OF UNIVERSITIES AND CNRS
Inria in the world
In 2017, 90 associated teams
with laboratory abroad

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Technology transfer
5
TRANSFER
OF TECHNOLOGIES
AND EXPERTISE
Joint laboratories
(joint labs, innovation labs, labcoms)
R&D partnerships
(collaborative projects)
Technology transfers
(software and patents)
START-UP
CREATION
140 start-ups
including 75% in activity
or bought out
3,000 jobs created
• providing them with structural
assistance (IT-Translation)
• facilitating funding support
• working in partnership and
through networks (regional
incubators)

6. Outline
- J. Sainte-Marie6
• General considerations
★ AI in France and at Inria
★ Data & models
★ Emerging fields
• Energy
★ Production
★ Consumption
★ Optimization

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Cédric Villani
Mathématician
(Fields medal 2010)
& member of French
Parliament
Marc Schoenauer
Senior scientist
Inria
Report on Artificial Intelligence
4 strategic sectors
‣ health
‣ environment
‣ transport - mobility
‣ defense - security
7
Multidisciplinary institute
for Artificial Intelligence
(3IA)
‣ Few institutes
‣ Academic & Industrials

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Key messages of the document
6 parts
• Part II - For an agile and diffusive research
• Part IV - Artificial Intelligence at the service
of green business & environment
5 focus
• Focus 2 - Health at the time of AI
• Focus 3 - Make France a leader in augmented
agriculture
• Focus 4 - A breakthrough innovation policy in the
transport sector at European level
• Focus 5 : AI at the service of defense and security
8

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French strategy on AI
• Many aspects (education, ethical, employment…)
• Inria coordinates the research activities of the global plan
• A first call in October 2018 for
Multidisciplinary institute for Artificial Intelligence (3IA)
• 4 selected projects
‣ Grenoble - MIAI health, environment, energy
‣ Nice - 3IA Côte d’Azur health, territorial development
‣ Paris - PRAIRIE health, transport & environment
‣ Toulouse - ANITI transport, environment, health
9
• Inria participates to three of them
• October 2nd, INAUGURATION OF PRAIRIE

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Coupling of data & models
• a large amount of measurements
• the dynamics is governed by math. models
• interest of coupling data & models
10
Optimization techniques
• new # stable
• predictive models & simulations
for optimization
• convex/nonconvex, high dimension, graphs
Emerging fields
• optimized/sustainable agriculture
• proteins or biomass production
• certified AI, military applications
• paintings…
∂X
∂t
+ ∇ ⋅ F(X) = 0
Sea Surface Temperature (SST)

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A lot of data available
•Physical laws constrain evolution
•Humanities & social sciences
•Deterministic versus stochastic
11
Sophisticated models required
Coupling & numerical approximation
•More and more accurate
•Many quantities measured
•Data assimilation, data-driven modeling
•AI & physical/social models
•AI + Num. methods, forecasting & certification

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Digital world & energy consumption
12
Digital world = 14% of the overall consumption in 2017
MYRIADS

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• ICT : part of the problem & part of the solution
★ a lot of expected and announced benefits
★ but growing criticism (raw materials, energy consumption…)
• Second-round or rebound effects
★ e.g. optimization implies savings of energy but favors development
• Emergency versus acceptability
★ commitments given by France (carbon emissions, footprint)
★ very long term objectives
• Economic uncertainties
★ oil price, raw materials
• Asymptotic behavior / saturation
• Sociological aspects
★ scientific approach of future generations?
★ digital & reduction of inequalities
13
Environment & sustainable dev. vs digital

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« La recherche et l'innovation doivent apporter leur
concours à la préservation et à la mise en valeur de
l’environnement. »
« Research and innovation must support the
preservation and enhancement of the environment. »
14
A riddle

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« La recherche et l'innovation doivent apporter leur
concours à la préservation et à la mise en valeur de
l’environnement. »
« Research and innovation must support the
preservation and enhancement of the environment. »
15
A riddle
French constitution - Charter of the environment - Article 9

16. Few
contributions
- J. Sainte-Marie16
• Research results, application oriented
★ not an exhaustive list
★ few focus
★ domains where AI is already
developed or not yet
• Energy
★ Production
★ Optimization tools

17. Energy
production/storage
- J. Sainte-Marie17
• Solar energy
• Wind turbines
• Marine energies
• Biomass

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Best simulation
(european center, ECMWF)
Satellite observation
(data base HelioClim)
Our prediction
W/m2W/m2 W/m2
ANGE
• Sequential learning:
‣ several simulations of incident solar radiation on the ground
from different weather models are combined
‣ the weights of the linear combination evolve in time
• Collaborations with EDF R&D (test on photovoltaic parks) and
MétéoFrance (for wind)
• For long-term & short term simulations
Photovoltaic panels
• Also simulation of light trapping in solar cells
NACHOS
EoCoE european project

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• Several meteorological models
‣
‣ At time T, N predictions available
• Instead of choosing the best candidate among the
• A linear combination of the predictions
• The coefficients are attained from
‣ neural network
‣ bayesian approach
• Can be also used for wind farms, wave propagation
ℳ1, …, ℳN
Xi(T) = ℳi(T)
Xi(T)
̂X(T) = ∑
N
i=1
ωiXi(T)
{ωi}
Photovoltaic panels

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MEMPHIS
• Collaboration with VALOREM
‣ Optimization of blades for wind
turbines
‣ Coupled simulations
★ fluid: 3d Navier-Stokes (LES)
★ structure: BEM model
‣ Assembly of a mast of wind
measurements, deformation and
pressure sensors on a blade
Optimization/design of wind turbines

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MULTISPEECH, TOSCA
• Constraints for wind farms
‣ noise emissions
‣ structure safety
• Beyond « static » recommandations
• Acoustic control of a wind farm
‣ new paradigm of real-time control
‣ real-time estimation of the residual sound
level and wind turbine level by sources
separation
‣ dynamic environment, many uncertainties
Wind turbines (control/optim.)

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Marine energies
22
ANGE, CARDAMOM, I4S
• Kinetic energy converter
‣ Already operational (Gironde river)
‣ Stability of the structure
‣ Impact over aquatic life
• Wave energy converter
‣ Active control & prediction (image processing)
• Optimization and design by num. simulations

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BIOCORE, DYLISS, ANGE, IBIS, COMMANDS, McTAO, PLEIADE
• Microalgae
‣ richness in proteins, lipids, vitamins, antioxidants...
‣ biofuel, chemistry, cosmetics...
‣ human or animal nutrition
‣ better yield than rape
• Inria Project Lab
‣ "Algae in silico" : from the gene to the industrial process
‣ 7 Inria teams involved
‣ INRA (LBE and LIPM), CNRS-SU (LOV), IFREMER (PBA),
CentraleSupelec (LGPM)
Culture of microalgae

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A multidisciplinary project
• AI for protein function prediction

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• Storage of gaz, pollutants, nuclear waste
• Collaborations with:
★ ANDRA, BRGM, IFPEN,...
• Simulations
★ multi-physics (thermal, hydraulic,
chemical, radiological…)
★ multi-scale, in time and space
★ 1 million year
• Darcy’s equation
• AI not yet very developed
★ but estimation of tensor D
★ …
∂θ
∂t
+ ∇ . (D∇θ + K)
25
SERENA, COFFEE, RAPSODIE
Flows in porous media

26. Energy
optimization
- J. Sainte-Marie26
• Optimization in a broad sense
★ minimization of a cost function,
of energy consumption
★ do better than previously

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SIERRA,
THOTH,
WILLOW
•Algorithm and theories as generic as possible
‣ dominant in the last 30 years
‣ few assumptions concerning the problem to solve
‣ easily replicable to other areas
•Interdisciplinary collaborations, specific prior knowledge
‣ simple is often already done
‣ cancer diagnosis vs automated driving
‣ collaborations between mathematicians, data analysts and
physicists, biologists, economists…
Machine learning/AI: two main approaches

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SIERRA,
MISTIS
•A research domain
‣ BONUS, CELESTE, GEOSTAT, INOCS, MISTIS, MODAL, RANDSOPT, REALOPT,
SEQUEL, TAU
•Minimization of (convex) functions
‣ choice of the norm, from min max to min min
‣ acceleration of num. techniques (gradient descent, regularization…)
‣ Wasserstein distance, optimal transport
‣ sparcity (L1 norm)
•Mathematical statistics & learning
‣ develop robust, accurate statistical estimators
‣ large dimension
Optim. / learning & statistical methods
CELESTE,
SEQUEL

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TAU
BONUS, FUN, INOCS, MYRIADS, RANDOPT, REALOPT
•Management and optimization of electricity demand,
decision support system for energy management
‣ intermittence, available ressources, constraints, price
•Collaborations with ADEME
‣ optimization of large energy systems
‣ optimal planning from long periods to daily maintenance
‣ taking into account storage means (hydraulic, …), stochastic
uncertainties (weather, market prices, demand prediction)
‣ an efficient optimization algorithm proposed using deep networks
Smart grids, optimization

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LACODAM
• Supplier / customer relationship
‣ provider: pricing, optimization at the residential scale
‣ customer: change in demand based on price
• Customer behavior
‣ search of time series data from sensors in industrial
context
‣ make sense from large quantities of data
‣ automating data science workflow discovery
‣ both data mining and AI
MAVERICK,
INOCS
Smart grids, optimization (cont’d)

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MAVERICK
Consumption in a domestic environment
• Activelec: visualization tool for non-expert users
• "eco-feedback": what happens if... ?
‣ I turn off the devices instead of leaving them on standby
‣ I change the program of the washing machine
‣ (data-driven) sociological models

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TOTH, PANAMA,
ROMA, ALPINES
Taking into account energy consumption in the design of
new algorithms, architectures & langages:
• High-performance and energy-efficient neural networks
• Learning memory and energy efficient dictionaries
• HPC: fast and stable numerical schemes, high order ?
• Deep neural network & numerical analysis
• Compiler optimizations
• Parallelization
• Micro-architectural features
• GPU, many-cores, FPGA
• Also energy efficient embedded systems
Lean ICT
CASH, CORSE, PACAP
SPADES, CAIRN

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MYRIADS
AVALON, DATAMOVE, ROMA, STACK,...
Minimization of energy consumption at the system or network
level
• multi-objective scheduling: compromise between energy cost,
availability, degradation of service
• shutdown policies in data centers, HPC centers,...
Green IT
• distribute the load to adapt to
the geography of renewable
energies

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DYOGENE
Energy storage in batteries
• Expensive, several parameters
‣ cycle of life
‣ calendar life
‣ electricity price
Electricity price variations
Li-ion cycle of operation with DoD
• Result: a numerical algorithm
based on optimal control under
constraints
• Three objectives
‣ maximise life time of batteries,
minimize raw materials needs
‣ storage at the minimal price

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AGORA, FUN, SOCRATE, SUMO
35
• Fault detection & adaptation
• Routing in Software Defined Networks: 5 to 35% savings on
networks of Internet Service Providers (soft: Graph & Sagemath)
• What is the economy by exploiting the fact that users are ready for
service degradation?
COATI, RESIST, MADYNE, MYRIADS
• IT & constraints (heterogeneity, default,
crisis…)
‣ self-organization Future Ubiquitous Network
‣ taking into account an assortment of
networks (wifi, ad-hoc, cellular, bluetooth)
‣ smart cities
‣ survivors alternately broadcast alert
messages
Consumption in networks

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• Poaching in Kruger Park
350km x 60 km, South Africa
• Precision farming
• Agriculture: collecting soil
information via wireless sensors
• Adaptive feedback protocol with
low energy consumption
• Techno 6TiSCH:
★ Consumption of a sensor:
10 years with 2 batteries AA
★ Network up to 50,000 nodes
FUN
EVA
Low consumption networks

37. Conclusion
&
Summary
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• Interaction between massive data and models/simulations
‣ data-driven modeling
‣ machine learning
• Energy : production, transport, consumption, networks
‣ optimization, pricing
‣ multi-scale, multi-physics, uncertainties
• Include energy criteria in algo., architectures, compilers
• A lot of contributions, not an exhaustive list
• A technology used in a domain can be adapted to another one
• Do not hesitate to us
38
Energy:
data, models, algorithms & sustainable development

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Socio-economic areas of application
39

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Budget 2018
INITIAL BUDGET
€ 230M
GRANT
FOR PUBLIC
SERVICE CHARGES
€ 170M
OWN RESOURCES
€ 60M European Union
ANR outside PIA
PIA
French public contracts (excluding ANR and PIA)
Private contracts
Intellectual property, sale of products and services
Autres ressources
19
8
5
13
6
6
3
6
3

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Outstanding researchers
54 ERC grant laureates since 2007
41
Prix MicrosoftMédailles du CNRS
Serge
Abiteboul
Olivier
Faugeras
Gérard
Berry
Gérard
Huet
Gilles Kahn
deceased
Alain
Bensoussan
Nicholas
Ayache
François
Baccelli
Philippe Flajolet
deceased
Stanley Durrleman
Christine
Guillemot
Julien Mairal Jean-Baptiste
Mouret
Cordelia Schmid
Karthik Bhargavan
Wendy Mackay
Adrien Bousseau
Jan Ramon
Rachid Deriche
Maria
Naya-Plasencia
Cătălin Hritcu
Marie Doumic
Fabien LotteAnne-Marie
Kermarrec

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Joint laboratories
INNOVATION LABS & LABCOMS
42
Certivibe #HMI #OpenVibe #Certification
Colinocs #Optimization #Scientific modelling #Colisweb
#Logistics
Exapta #Decision-making tools #Planning #Combinatorial
optimization #Viticulture
Haptihand #Robotics # Interactive Motion Modelling
#Haptic perception
Leelco #Standardization # Security # Telecommunication
#Mobile standards
Meshing-lab #Mesh #HPC #CAO #Digital simulation
Oscar #Aeronautics #Software #Commands #Programming
Triton #Software factory #Social networks #Mobile
applications #Events organization
Voice Technologie #Sound processing #Image
processing #Scientific modelling
Cineviz #Animation # 3D Cinema #Tracking
ProofInUse #Automatic prover # Critical system
#Computer-assisted proof
SMILK #Web data #Linked open data #Natural-
language processing

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Our start-ups
150 renown start-ups, some acquired by market leaders
43
Sun Microsystems
The Mathworks
Yahoo IBM
EADS AutodeskAnsysBusiness Objects
Gemalto Dassault Systèmes
Apple

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Some great software
140 software packages per year
CAML COQ GATB
MUMPS NATRON
RIOT SAMSON

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Inria international Labs
Inria@SiliconValle
y
UNITED STATES
Joint-Laboratory on
Extreme Scale Computing
UNITED STATES
InriaChile
CHILE
LIRIMA
SENEGAL
EPFL-Inria
SWITZERLAND
LIAMA
CHINA
CWI-Inria
THE NETHERLANDS

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Inria in the world
90 associated teams active in 2017
Canada
4
USA
40
4
Chile
Senega
l
2
Tunisia
1
Brazil
8
India
3
2
China
1
Taiwan
Japan
5
Mexico
2
Argentina1
1
Europe
10
Cameroon
2
Morocco 1
South Africa
1
12
Gabon
Singapore
1

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Your interlocutors at Inria
47
TECHNOLOGY
&
TRANSFER
Joint laboratories
(joint labs, innovation labs, labcoms)
R&D partnerships
(collaborative projects )
Annual research report
The team leader or one the team members
A deputy director for science (see web site)
Technology transfer
(software or patent)
Transfer of expertise/know-how
(expertise, mobility)
SCIENCE
&
RESEARCH

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Strategic partnerships
48