with bio-inspired circuits · 2018. 8. 22. · mobileye eyeq4 mobileye eyeq3 movidius myriad 1...

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!