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Data Intelligence at CEA List:

All about Industry

Géraud CanetCEA List

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CEA DEEP MANTA TECHNOLOGY

Partiallyoccludedvehicles

Vehicleorientation

Localisation of vehicle

parts(eg lights)

Distance to camera

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BENCHMARK KITTI 2016: ORIENTATION ESTIMATION

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Experimentation in CEA site of Saclay

Collaboration with CEA List

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CEA LIST | AN ORIGINAL BUSINESS MODEL

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CEA LIST : Institute for Digital Transformation

At the heart of

Campus Paris-Saclay

> €80 M annual operating budget

75% external funding

700 employees

500 permanents

150 doctorants/post-doc

> 12 high-tech start-up created

420 priority patents portfolio

65 applications/year

100 customers

CAC 40 and SBF 120 (50%) SMBs and start-ups (40%) International (10%)

160 active licences

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ADDRESSING THE FULL SPECTRUM

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ADDRESSING THE FULL SPECTRUM

Raw Data Processing

SceneAnalysis

DistributedSelf-

adaptative Systems

Data Modelling

and visualisation

Close-to-Vision

SensorsArchitecture

Bio-inspiredArchitectures

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ADDRESSING THE FULL SPECTRUM

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Long term Research and innovation

Common Core(CEA)

AppliedTechnologies

(Partners)Lo

ng te

rm

Energy

Automotive

Manufacturing HealthTourism

Security

Excellence (benchmarks)

WikipediaMM 2011-12-13 DGA (15)

ImageClef (16) NIST : SED CMU-14

MEDIA EVAL

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Case study: an AI assistant for monitoring the prostate cancer

1. Mining heterogeneous & multi-sources Data• Patient record & family background• Medical tests (blood samples, genetic analysis) Analysis & Decision support

Diagnosis & Recommandations

2. Modeling medical expertise• Physician’s insights• Legal guidelines

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• A single platform to

• Explore Deep Neural Network (DNN) topologies

• Experiment state-of-the-art learning techniques with large databases

• Benefit from approximate computing to generate optimized DNNs

N2D2 PLATFORM

COTS targets

- C/C++- OpenMP- CUDA/cuDNN- OpenCL- C for HLS

Modeling Learning Code Generation

Evalua-tion

Data conditioning

Learning

toolbox

OptimizationLearning

& Test

databases

CEA acceleration IPs

- PNeuroprogrammable accelerator

- DNeuro FPGA accelerator

- Spike Based Neuro- Mixed Signal IC

•Accuracy

• Latency

•Watts

•Cost

• Form factor

• Time-to-market

Non-functional

parameters

Energ

y Efficiency

Tech

no

log

y A

cces

sib

ility

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FPGA version is already competitive with existing CPU & GPU solutions

• CNN Application ported on different Architectures

• PNeuro performance results and estimation

• First FPGA product developed for early 2017 by GST

• Embedded FPGA: Artix 100 (~1W), 17.6cm² for the board, including one cluster

PNEURO PERFORMANCE RESULTS

Measured

Post Backend Estimation

Extrapolated Estimation

Target Frequency Performance Energy Efficiency

Quad ARM A7 900 MHz 480 images/s 380 images/s/W

Quad ARM A15 2000 MHz 870 images/s 350 images/s/W

Tegra K1 850 MHz 3 550 images/s 600 images/s/W

PNeuro (FPGA) 100 MHz 5 000 images/s 2 000 images/s/W

PNeuro (ASIC) 500 MHz 25 000 images/s 125 000 images/s/W

PNeuro (ASIC) 1 000 MHz 50 000 images/s 125 000 images/s/W

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EUROPEAN INVOLVEMENT

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CYBERPHYSICAL SYSTEMS

CYBERSECURITY

ADVANCED COMPUTING BIG DATA

ARTIFICIAL INTELLIGENCE

One partner from CPS to AI

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Géraud CanetCEA List

geraud.canet@cea.fr

http://www-list.cea.fr/en/

Contact Details