Phase Change Memory as Synapsesleti.congres-scientifique.com/.../documents/9-LETI-Suri.pdf · 2011-08-24 · Manan Suri, Workshop on Innovative Memory Technologies Titre de la présentation

Phase Change Memory as Synapses

Manan SURI

LTMA

Advisor: Dr. Barbara DeSalvo (CEA-LETI)

co-Advisor: Dr. Dominique Vuillaume (CNRS)

Collaborator: Dr. Christian Gamrat (CEA-LIST)

© CEA. All rights reserved

Titre de la présentation | Date | 2 Manan Suri, Workshop on Innovative Memory Technologies, 29th June, 2011

Outline

Basics -The Big Picture

-Synapse and Plasticity

-STDP

Technology - Using PCM



Making intelligent hardware inspired from nature !

Applications - Artificial Vision - Pattern recognition - Robotics - Audio Analysis - Defense and Security - Satellite Imagery

Advantages of Neuromorphic Hardware - Massive Parallelism - Scalability - Inherent defect, fault and failure tolerance - Low Power



Hippocampus & Hypothalamus -region from where we take the inspiration

-Indefinitely reconfigurable -Enormously parallel

Brain Facts, The Society for Neuroscience, Washington, 4th edition,

2002

The Brain



Neuron

Pulse Frequency Signal Processing Device Input Frequency of pulses received from the pre-neuron Output Frequency of action potentials transmitted to post-neuron

Xiao-lin Zhang, Nature Precedings, 21st March,2008.1703.1 John Wiley & Sons Inc, 2000



Synapse

Spike arrives at synapse

Vesicles release neurotransmitters in the

synaptic cleft

Neurotransmitter bind to receptor sites

Control ion gate channels

Affect membrane potential Brain Facts, The Society for Neuroscience, Washington, 4th edition,

2002



Synapses are plastic !!

The efficacy with which they transmit action potentials (spikes) can either increase or decrease.

Efficacy – neurotransmitter/receptor kinetics, availability etc – short term effects

Increase in synaptic efficacy = Potentiation, LTP, STP

Decrease in synaptic efficacy = Depression, LTD, STD

Plasticity in Synapses



• Long term depression (LTD) Post spikes before Pre-neuron

• Long term potentiation (LTP) Pre spikes before Post-neuron

• ∆ t = t post – t pre

Spike Time Dependent Plasticity (STDP)

Relative timing of spikes between a Pre & Post neuron determine the change in plasticity of the synapse. Similar to the famous Hebbian learning rule. Responsible for long term effects-



Smaller the time lag, stronger is the potentiation or depression

LTD LTP



1.1 x 104

1.5 x 107

6.2 x 109

1.1 x 1010

Big Numbers!!

Roth G, et al, Evolution of the brain and intelligence,

Trends in cognitive science, May 2005, 250-7

Ndabahaliye, Anicia, Number of neurons in human

brain, The physics Factbook, 2002

Avg. synapses per neuron = 1000

Total synapses

1014 – 1015

Intel 8-core Xeon Nehalem- Ex

2.3 x 109



Hybrid Systems CMOS Neurons + Nanodevice Synapses

K Likharev, Hybrid Semiconductor – Molecular

Nanoelectronics, The Industrial Physist, AIP, July 2003

Greg, S, Snider,Spike-Time-Dependent Learning in

Memristive Nanodevices, ACM International symposium on

nanoscale architectures, 2008

Geoffrey Burr, IBM Almaden Research Center,

Storage Class Memory, April 2010



Technology at Core

Why chose Non-Volatile?

- Need to store the synaptic weights

- Need to change the strength of the communication channel based on activity

A device which can change and remember simultaneously

Candidates

- Phase Change Memory (PCM)

- Oxide Resistive RAM (OxRAM or RRAM)

- Conductive Bridge Memory (CBRAM) or Programmable Metallization Cell (PMC)



Phase Change Memory (PCM)

Phase change materials are being used for information storage since long. We exploit the difference in the properties of the crystalline and amorphous phases of the material

M. Wuttig and N. Yamada, Nature Materials 6, 824

(2007)

Main Advantages:

• Low voltage

• Fast read/write (10-100ns)

• High endurance (>1E12)

• Good scalability

• Low cost



Basics of PCM



Using PCM-I

LTP = SET = Decrease Resistance = Crystallize

LTD = RESET = Increase Resistance = Amorphous

200

2000

20000

200000

0 1 2 3 4 5 6 7 8

Re

sist

ance

[Ω

]

Pulse Amplitude [V]

50ns70ns100ns300ns500ns1us

LTD

LTP

Manan Suri, et. al, IJCNN-2011.



Using PCM-II

LTP

LTD

Manan Suri, et. IJCNN-2011.



400

4000

40000

0 20 40 60 80

Res

ista

nce

[Ω

]

Number of pulse

400

4000

40000

0 5 10 15 20 25 30

Res

ista

nce

[Ω

]

Pulse sequence number

1.5V_4us

6V_100ns

6.5V_300ns

Combinations of LTP/LTD

Manan Suri, et. IJCNN-2011

Using PCM-III



Implementing STDP with PCM-I

Mathew Joseph Breitwisch, et. al, IBM, Armonk, NY,US

Patent Publication 0299297/A1, Nov 25, 2010



Implementing STDP with PCM-II

Duygu Kuzum et. al . Nano Letters, June, 2011



Caution!! A reality check

We are not recreating the Brain – just getting Inspired from it - We cannot create something we don’t fully understand

- Targeting specific brain-like functions

Difference between Neuro-Mimetic & Neuro-Morphic -Time scales

- Learning rules

A single device and or a full scale-system - Pulse Schemes

- Power/Energy

Variability - Zero not tolerated

- How much is optimal??



We are not alone!!

DARPA- SYNAPSE (2008-2016)

Systems of Neuromorphic Adaptive Plastic Scalable Electronics

HP

HRL

IBM Almaden, IBM Watson

Stanford, Cornell, U-Wisconsin, Columbia, Boston Univ.



EUROPEAN- FACETS

CHINA BRAIN PROJECT

(4-year long project)

We are not alone!!

Fast Analog Computing with Emergent Transient States

EU Future Emerging Technologies (FET) initiative

Austria, France, Germany, Hungary, Spain,

Sweden, Switzerland, and United Kingdom

“Now that all the building blocks are available, our next task is to start

building functioning systems out of them.” – DARPA SYNAPSE – April,2011.



Conclusions

- Collaboration with CEA- LIST – Large Scale Neuromorphic Systems

- Material Study GeTeN, GeTeC, interfaces…

- Cell Optimization – Learning Performance

On- going & future direction…

PCM is promising for emulating synapse-like behavior

IEDM-2011 (submitted)

PCM for Complex Visual Pattern

Extraction



Acknowledgements

-Barbara DESALVO

-Christian GAMARAT

-Olivier BICHLER

-Damien QUILEROZ

-Luca PERNIOLA

-Veronique SOUSA

-Dominique VUILLAUME

-Ludovic POUPINET

Thank you for your attention!!

Questions/Comments??

Documents

Phase Change Memory as Synapsesleti.congres-scientifique.com/.../documents/9-LETI-Suri.pdf · 2011-08-24 · Manan Suri, Workshop on Innovative Memory Technologies Titre de la présentation