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Applications of Memristors in ANNs
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OutlineOutline
• Brief intro to ANNsBrief intro to ANNs
• Firing rate networksSi l l t i t– Single layer perceptron experiment
– Other (simulation) examples
• Spiking networks and STDP
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ANNsANNs
ANN is bio‐inpsired massively parallel network,ANN is bio inpsired massively parallel network, i.e. directed graph, with nodes acting as neurons and edges acting as synapses. The functionality is learned during training phase by changing weights of synapses
• By topology• By learning paradigm• By coding neural informationBy coding neural information
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Very good reviewVery good review
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Applications
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ChallengesComplexity
~ 1011 neurons
~ 1015 synapses 10 synapses
Connectivity
~ 1 : 10000
i ll liMassive parallelism
100 steps long rule: few to several hundred hertz; face recognition i 00in ~100 ms
2‐3 mm think , 2200 cm2
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McCulloch‐Pitts neuron
d ff f
1943
different activation functions
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By topologyBy topology
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By learning paradigm
l lKey questions: Capacity, Sample complexity, Computational complexity
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By information codingBy information coding
• Firing rate vs spikingFiring rate vs spiking models
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Perceptron: Main idea
xBias, x0
Single layer perceptron
x1x2x3
w1 w0
]sgn[9
0
i
ii xwy
x9w9
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Hebbian ruleHebbian rule
• Learning usingLearning using local information
• Orientation• Orientation selectivity
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Multilayer perceptron
Key questions: number of layers, number of hidden neurons
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BackpropagationBackpropagation
Gradient descent method to i i i t f timinimize cost function
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Competitive learningCompetitive learning
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Learning binary patterns with kcompetitive network
Instar learning law:
What happens if more than four unique patterns are presented? q p p
What happens when all white pattern is presented?
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Complementary codingComplementary coding
• Resolve no signal issue for a particular (instar) learning lawlearning law
• How to learn invariance? (translation, size, angle etc.)
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With added complex cellsWith added complex cells
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With added complex cellsWith added complex cells
• AND in bottom layer OR in top present one hot• AND in bottom layer, OR in top, present one hot patterns to the top layer
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Perceptron: Main idea
x1x
x4x
x7x x = +1x1
x
Bias, x0
w1
Single layer perceptron Binary pixel array
hw bottleneckx2x3
x5x6
x8x9
x = –1x2x3
w9
w1 w0
]sgn[9
0
i
ii xwy
x9w9
Considered training/test patterns
Pattern “X”, class d = +1Perceptron training rule: ∆wi = αxi(p)(d(p)‐y(p))
V
Crossbar implementation
V ∞ x G+-G- = G ∞ w
[I+ I ]
AI+
V0 V1 V9V2
G0+ G1
+ G2+ G9
+
Pattern “T”, class d = –1+ ‐
y = sgn[I+-I -]param. analyzer‐based
Alibart et al., submitted, 2012AI–G0– G1
– G2– G9
–
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Windrow’s memistorAdaLiNe concept … … and hardware implementation
BernardWidrow
MarcianHoff
B. Widrow and M.E. Hoff, Jr., IRE WESCON Convention Record, 4:96 1960
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Pt/TiO2‐x/Pt devicesg = I(0.2V)/ 0.2 V
25 nm Au / 15 nm Pt top electrode
1.0
)
=
Pt top electrode
5 nm Ti / 25 nm Pt bottom electrode
e‐beam patterned Pt protrusion
30 nm TiO2‐xS
0
rent (m
A)
20 nm
‐ Any state betweenON and OFF
‐ In principle dynamic
‐1.0
Curr S
A
V
‐ In principle dynamic system with frequencydependent loop size but ….
‐1.0 0 1.0Voltage (V)
A‐ Strongly (superexp)nonlinear switching dynamics
‐ Gray area = no changeVoltage (V)+Vswitch‐VswitchAlibart et al.,
submitted, 2012
Gray area no change ‐ State defined within
gray area
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Switching dynamics
RESET: R =Rd
setvoltage initialize to R0FF
10
100
RESET: R0=RON
SET: R0=ROFF
reset
read
time initialize to R0N
1
10
R/R
0
‐ Small pulse amp = finer state change butmay require exp long time
‐ Large pulse amp faster but at cruder step
1E 8
0.11E-4
-0.9VmV
(A) -0.5V to -0.8V
1E-81E-6
1E-40.01
1
-1.5-1.0-0.5
0.00.5
1.01 5 Tim
e (s)
Pulse voltage (1E-5
-1.0V
-1.1V
-1.2V
-1.3V
Cur
rent
@ -2
00
1.5 Timge (V)
F. Alibart et al. Nanotechnology, 23 075201, 2012
0 1x10-5 2x10-5
Time (s)
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Nonlinear switching dynamics
effective barrier modulation due to:
heating
electric field
1
2 ion hopping
e‐
ion hoping
z+z+e‐
electrodeelectrode
UA
~Eaq/2
~ kB∆T
initial profile
2
1
eoxidation reduction‐+ v
Eaq/2
energy a∆UA
h t iti d ti3
3
2
hop distance
position
phase transition or redox reaction3
J. Yang et al. submitted 2012
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Speed vs. retention
linear ionic transport linear ionic transport pp
TI
I
write
store ~)()0(
VV
DV
Vvv
nonnonlinearlinear effect due to temperature and/or electric field
)(~ writeB
A
storeB
A
store TkU
TkU
eeVV
e.g. temperature only:
Twrite V
D.Strukov et al. Appl.Phys.A 94 515 (2009)
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Switching statistics
RESET SET
10-4
mV
(A)
10-4
0mV
(A)
10-5
urre
nt @
200
m
10-5
Cur
rent
@ 2
00
0.02.0x10-6
4.0x10-6
6.0x10-6
8 0x10-6
0.60.8
1.01.2
Cu
ve tim
e (s)
Voltag
0.0
5.0x10-7
1.0x10-6
1.5x10-6
-1.4-1.2
-1.0
ative
time (
s)
Voltage8.0x101.0x10-51.4
Cumula
tivetage (V)
5 0
2.0x10-6-0.8
-0.6 Cumula
tage (V)
10 TiO2‐x devices
Alibart et al., submitted, 2012Large switching dynamics dispersion!
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Variations in switching behavior
101.0
g = I(0.2V)/ 0.2 V
10
g INIT
IAL
‐1.0
0
Curren
t (mA)
1
gAF
TER/g
write‐1.0 0 1.0
Voltage (V)SET
10 1
Syn
S =readtune
RESET
-10
1
0.1
1 ulse voltage (V)
ynaptic weight
gINITIAL (mS
SET1
Pulsht,mS)
Alibart et al., submitted, 2012
RESET‐ Continuous state change
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Tuning algorithmWrite
apply pulse VWRITE
Processing
VWRITE = VWRITE + sign * TVSTEPoldsign = sign
Processing
Is state reached
Start
(inputs: desired state Idesired, desired accuracy
A
Read
Processing
check for overshoot and set the i f i t i
within required precision, i.e. (Idesired – Icurrent)/ Idesired < Adesired ?
Adesired; initialize: write voltage to small non‐disturbing value VWRITE = 200 mV, voltage step TVSTEP = 10
V
(apply VREAD = 200 mV and read current Icurrent)
sign of increment, i.e. sign = Icurrent ‐ Idesired ;
if VWRITE !=VREAD and sign !=oldsign then initialize VWRITE =
200 mV
no
yes
Finish
mV;
Intuitive algorithm Implemented algorithmvoltage
0read
set timevoltage
0
set
time
Intuitive algorithm Implemented algorithm
resetread
resetread
non‐disturbing pulse F. Alibart et al. Nanotechnology, 23 075201, 2012
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High precision tuning
120AIncrease WeightDecrease Weightvoltage
set time
1E-4
60A
mV
(A)
Increase WeightStand-by (Read only)0
resetread TiO2‐x devices
(w/o protrusion)
( )/
30A
t @-2
00
32
100(gdes‐gact)/gdes<1% ~ 8‐bit precision
1E-5Cur
rent 15A
29
30
31
0 1000 2000 3000
1E 57A
950 1000 1050 1100 115028
29
0 1000 2000 3000
Pulse Number F. Alibart et al. Nanotechnology, 23 075201, 2012
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Limitation to tuning accuracy: Random telegraph noise
3
5k 5k
g p
10-9
10-8
0 2 4 6 8 10
1
2
.u.)
4k
Hz-1
)
4k 2k 1k 0.5k
R/R
(%)
Resistance (k)
10-11
10-10
Cur
rent
(a
2k
1k
PS
D/I2 (H
Resistance (k)
102 103 104
10-12
10C
0.2 0.4 0.6 0.8 1.0 1.2
0.5k
P
0.2 0.4 0.6 0.8
Time (s)
Time (s) Frequency (Hz)
‐ Solid‐state electrolyte (electrochemical) are noisierThe higher R the larger is noise
Ligang Gao et al, VLSI‐SoC, 2012
‐ The higher R, the larger is noise‐ For a‐Si limit to ~5‐6‐bit precision (but no optimization)
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Perceptron experimental setup
Vt
Switching matrix( l )
Arbitrary waveform generator B1530
A
(Agilent E5250A)
Current measurementB1530 (fast IV mode)
Ground (GNDU, Agilent)
Agilent B1500
Wires implementing crossbar circuit
Agilent B1500
Chip packaged wire bonded memristive devices
Alibart et al., submitted, 2012
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Perceptron: Ex‐situ trainings1
Evolution of synaptic conductance upon sequential tunings2
v s10 5
0.6
mS
)
+ tuning
final weights after programming
weight import accuracy ~10%
y p p q g
+ it
read pulse write pulse0.3
0.4
0.5
wei
ght,
g (m g+ tuning g ‐
123456
gi+, i
gs2
0 20 40 60 80 100 120 250 3000.0
0.1
0.2
Syn
aptic
w
weight slightly affected by half‐select problem
678910
v
t
+Vswitch
-Vswitch
v
t
voltage at g8- 0 20 40 60 80 100 120 250 300
Pulse number #
‐ Crossbar half‐select tricklf l d d i li h l ff d ( bi i i )switch
Alibart et al., submitted, 2012
‐ Half‐selected devices slightly affected (>5‐bit precision)
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Perceptron: In‐situ training
V tra in = 1 VV tra in = 0 .9 V
s1 s2
g1+ g4
+
Evolution of synaptic conductance upon parallel tuning
‐ Four steps‐ α (V g)
∆gi ± = ±αxi(d(p)‐y(p))
0 05
-0 .10 .00 .1
-0 .050.00
g
g
s3s4g1
- g4-
s1=PSx=+1 voltage at g1+
‐ α (V, g)
0.000.05
-0 .050.000.000.05
g
(mS
)
g
g
g
+Vtrain/2v
t1 2 3 4
v
t
1 x=+1
s2=PS 1
voltage at g1
voltage at g1-
-Vtrain-Vtrain/2
0 1
-0 .20
-0 .15-0 .150.000.15
g
g
g
g
v
t
v
t
s2 PSx=‐1
s3=PS+d=+1
voltage at g1
voltage at g4+
0 00.1
-0 .15-0 .10-0 .05
0.00 .1
g
g
g
v
t
v
t
3 d=+1 g g4
voltage at g4-s4=PS‐d=+1
0 4 8 1 2 1 6
0.0
T ra in in g e p o c h
v
t
v
t+Vswitch
-Vswitch
4 d 1
Alibart et al., submitted, 2012
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Results
10
XT
initialInitial (random
XT
initial
Ex‐situ In‐situ
0
10
accuracy ~ 40%
( a doweights)
weight import accuracy ~40% 0
10T
ns
0
10
of p
atte
rns
accuracy ~ 10%
accuracy ~40%
weight import
10
0
ber o
f pat
tern after 10 epochs
with Vtrain =0.9V
0
10
Num
ber o
accuracy ~ 2%
accuracy ~10%
weight import 10
0after 7 more epochs with Vtrain =1V
Num
b
0
10
accuracy 2%weight import accuracy ~2%
-0.0002 0.0000 0.00020
10
train
-0.0002 0.0000 0.0002I+ - I- (A) I+ - I- (A)
Alibart et al., submitted, 2012‐ 3‐bit is enough for considered task
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Big picture
add‐on
Ti h i i i h CMOS l i (CMOL)
CMOSstack
Tight integration with CMOS logic (CMOL)Multi‐layer perceptron network
x1
x ywj1
x1gj1
gj2
weight memristor
x3
x2 yj
wj2
wj3 x3
x2gj2
gj3
‐+
jii
i gx
CMOS CMOS cell
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Spiking Networks and Spike‐Timing Dependent Plasticity (STDP)Dependent Plasticity (STDP)
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Spiking vs. firing rate neural networksFiring rate (average frequency matters, high frequency level 1, low frequency level 0)
Spiking networks
Relative timing of h ikthe spikes matters
Delay between neurons matters Enriches the functionality
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Spiking neural networks
Spatiotemporal processing
Known to happen in biology, d i h di i fe.g. detecting the direction of
the sound with two sensors and two neurons
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Polychronization: Computation with kSpikes
• According to Izhikevitch: Accounting for timingAccording to Izhikevitch: Accounting for timing of spikes allows to increase the capacity of the network beyond that of Hopfield networksnetwork beyond that of Hopfield networks
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Hopfield Networks
Binary Hopfield network
])(sgn[)1(0
i
ijij tvwtv
Capacity is pmax = N/logN
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Polychronization: Computation with Spikes
Due to STDP system can self‐organized to activate various polychronous groups
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Spike Timing Dependent Plasticity
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STDP Implementation (first attempt)STDP Implementation (first attempt)
“ h i l t d CMOS“… we have implemented a CMOS neuron circuit to convert the relative timing information of the neuron spikes into pulse width p pinformation seen by thememristor synapse
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STDP Implementation Proposal for Memristors
Assumed rate change as a function of applied voltage
Proposal for Memristors
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STDP Implementation with PCM
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Long Term Depression and Short Term PotentiatingPotentiating
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Electronic Pavlov’s Dog
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Snider’s Spiking NetworksSnider s Spiking Networks
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Example: Network Self-Organization
(Spatial Orientation Filter Array)(Spatial Orientation Filter Array)
adaptiveadaptiverecurrentnetwork
+‐ output
xi
+ ‐‐‐ ++
49
input
G. Snider, Nanotechnology 18 365202 (2007)
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