with bio-inspired circuits · 2018. 8. 22. · mobileye eyeq4 mobileye eyeq3 movidius myriad 1...
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TOMORROW’S ARCHITECTURESWITH BIO-INSPIRED CIRCUITS
BARBARA DE SALVOCEA-Leti
THE BRAIN AND AI
BRAIN WIRING
https://www.youtube.com/watch?v=atLQVgUwnrY
LEARNING
Dehaene-Lambertz et al., Science, 2002; PNAS, 2006; Brain and Language, 2009Lecture by Dr. Stanislas Dehaene on "Reading the Brain“
https://www.youtube.com/watch?v=MSy685vNqYk
Two-month old infant Adult (reference)
“MRI and M/EEG studies of the White Matter Development in Human Fetuses and Infants: Review and Opinion”, Jessica Dubois et al., , Curr Biol. 2013 October 07;
23(19): 1914–1919. doi:10.1016/j.cub.2013.07.075.
Visual Word Form Area
ENERGY BUDGET
ARTIFICIAL INTELLIGENCEDartmouth College (1956)IBM Deep Blue / Deeper Blue Chess Program (1996)Electronic Game Characters _ Sims (2000)
MACHINE LEARNING
DEEP LEARNINGNatural Speech RecognitionWaymo Level 4 Automated Driving System (2017)AlphaGo Zero (2017)
AI
Email Spam filter (1996)IBM Watson (2011)Amazon Recommandations
UBIQUITOUS COMPUTING
Gateway
Cloud DB
Gateway
Gateway
Cloud Layer
Edge/Fog Layer
End Devices
Cloud Layer
Safety
Privacy
DISTRIBUTED INTELLIGENCE
LEARNING AND INFERENCE
Bandwidth, Cost
CURRENT AI HARDWARE
Source: Moor Insights & Strategy – March 2017
QualcommSnapdragon
FPGAIntel & Xilinx
Supervised
Learning
MobilEye EyeQ5
MobilEye EyeQ4
MobilEye EyeQ3
Movidius Myriad 1
Movidius Myriad 2
10
100
1000
10000
1,00E+01 1,00E+02 1,00E+03 1,00E+04 1,00E+05
IntelIntel
201165nm
201628nm
2014 40nm
2020 7nm
2018 28nm
MOORE’S LAW & ARCHITECTURE
IMPROVEMENTS
100W
1W
IntelC
om
pu
tin
gEf
fici
en
cy(G
OP
S/W
)
Computing Performance (GOPS)
TREND FOR EMBEDDED AI PLATFORMS
Drive PX2
Drive PX2 Parker
Drive PX2 Xavier
Jetson TX1
Jetson TX2
10
100
1000
10000
1,00E+02 1,00E+03 1,00E+04 1,00E+05
Nvidia
Nvidia
10W
201520nm
201716nm
2016 16nm
2016 16nm
2017 16nm
100W
MOORE’S LAW &ARCHITECTURE IMPROVEMENTS
TREND FOR EMBEDDED AI PLATFORMS
Co
mp
uti
ng
Effi
cie
ncy
(GO
PS/
W)
Computing Performance (GOPS)
v
1E+00
1E+01
1E+02
1E+03
1E+04
1E+05
1E+06
1E+07
1E+08
1E+09
1E+10
1E-02 1E-01 1E+00 1E+01 1E+02 1E+03 1E+04 1E+05 1E+06 1E+07 1E+08 1E+09 1E+10 1E+11
Co
mp
uti
ng
Effi
cie
ncy
(GO
PS/
W)
Computing Performance (GOPS)
Human Brain(20W)
15
1314
16 12
79 8
1110
Volume
Prototype
Design
Academic
PERFORMANCE/POWER REQUIREMENTS10W1W100mW1mW<100µW
18 2
17
1
3
45 6
Honey BeeBrain
Intelligent End Devices
ADVANCED TECHNOLOGIESFOR NEUROMORPHIC HARDWARE
v
SPIKING NEURAL NETWORKSCOMMUNICATION SPIKING NEURONS UNSUPERVISED LEARNING
ADDRESS EVENT REPRESENTATION LEAKY INTEGRATE-AND-FIRE
SPIKE TIMING DEPENDENT PLASTICITY
v
Sony GPS28nm FDSOI (10mW) STMicroelectronics 2013
NXP Application Processor
28nm FDSOI Samsung
2014- 2016
Casio G-SHOCK GPW-10002014
MOBILEYEQ4Autonomous Driving
2015
Phytec SOM2016
FULLY DEPLETED SOI
Gate
DrainSourceUltra-thin Buried Oxide
DYNAPSELProcess 28nm FDSOI (STMicroelectronics)
Supply Vontage 0.73V-1V
IO Number 176 + (internal 59)
Chip area 2.8mm x 2.6mm = 7.28mm²
On-line Learning
Core Numbers4 non-plastic cores (each: 256 neurons, 16k TCAM-progr synapses)1 plastic core (64 neurons, 8k plastic synapses, 8k progr synapses)
Neuron Type Analog AExp I&F
Non-plastic Synapse TCAM based 4-bit weight
Plastic Synapse Linear 4-bit weight
Throughput of Router 1G Events/second
Synaptic Operation per Second per Watt 320 GSOPS/W
Scalability On chip progr. router for 16x16 chips
50pJ/spike
128kbit CBRAM
Everspin 64Mb
DDR3 STT-RAM Avalanche Technologies STT-MRAM 32Mb
RESISTIVE MEMORIES
8-bit controller withembedded ReRAM
UNSUPERVISED LEARNING WITH PCM-SYNAPSES
Recorded Stimuli Neuron-4th lane Neuron-5th lane
PCM 70 neurons4M PCMs
92% avg detection rate112µW
OXRAM-SYNAPSES FOR BIO-SIGNAL SORTING
OXRAM
3D TECHNOLOGIES
~103 3DC/mm²=> Core Partitioning
Manycore & HPCActive Interposers
TSV + µBumpPitch : ~20 µm
3D MonolithicCoolCubeTM
Pitch : 0.05-0.1 µm
108 3DC/mm²=> Logic Level Connection
Cortical Columns
Cu/CuPitch : 2-5 µm
~105 3DC/mm² Sub-block Partioning
HD-TSVPitch : 1-3 µm
Neural NetworksSmart
Imagers
1st layerInput 2nd layer
RETINE - A 3D Stacked Back Side Illuminated Vision Chip
Controlled
- Image Sensor
– Multi-core Processor
Parallel computing by exploiting in-focal-plane pixel readout circuitsVery high frame rate (5500 fps), without reducing ADC resolution
FUTURE OPPORTUNITIES
EMBODIMENT
Honey Bees
Wood Cricket
Credit: Avarguès-Weber and Giurfa. ©2013 The Royal Society
COGNITIVE CYBER PHYSICAL SYSTEMSComponents
• Smart Event-Driven SensorNetworks
• Actuators
• Situation AwareMulti-ModalPlatforms
• Low Power SNN Processors
• EnergyHarvesting
• Cyber Security
Cognitive CPS
Functionalities
• Learning from
External Stimuli
• Making Decisions
• Adapting to
Changes
• Executing
• Computing with
Intelligent
Algoritms
Leti’s 360Fusion
AUTONOMOUS SENSORY MOTOR ROBOTS
Mapping predictions
Real Time Interface to Control the Prosthetic Limb
Sensory Feedback
EMBEDDED NEUROMORPHIC BCI
Neural Activity Acquisition Decoding/Processing via Neuromorphic Systems
Low Power Embedded SNN at the Electrode Site
CONCLUSIONS
New emerging technologies will enable implementation of ultra-low power brain-inspired hardware and distributed intelligence
Full potential of brain-inspired technologies will be reached through a transformative holistic research approach
This will open the way to unforeseen new applications where a sophisticated system-environment dynamics will take place
CONCLUSIONS
ACKNOWLEDGEMENTSS. Bonnetier, E. Beigne, S. Catrou, E. Vianello, T. Dalgaty, A. Valentian, P. Vivet, M. Causo, S. Cheramy, P. Batude, F. Andrieu, M. Vinet, G. Molas, J. Hoine, L. Di Cioccio, F. Simoens, M. Belleville, C. Reita, D. Dutoit, A. Hihi, S. La Barbera, D. Morche, G. Sicard, A. Molnos, F. Heitzmann, L. Poupinet from CEA-Leti
M. Duranton, C. Gamrat, A. Dupret, O. Bichler from CEA-List
A. Jerraya from CEA-DRTB. Yvert from INSERMProf. G. Indiveri from University of Zurich and ETH ZurichProf. J. Casas from University of ToursProf. S. Mitra from Stanford University
Images in the movie :- Bilayered Brain Structure: S. Crandall & B. Connors, Brown University- Cortical Columns: Blue Brain Project / EPFL ©2005 – 2018. All rights reserved
Thank you for your attention!