Pattern Classification with Pattern Classification with Memristive Memristive Xbar Xbar Circuits Circuits Dmitri Strukov UC Santa Barbara Acknowledgments: Fabien Alibart, Elham Zamanidoost, Brian Hoskins, Gina Adam, Farnood Merrikh‐Bayat, Xinjie Guo, Ligang Gao, Christof Teuscher, John C th Ti Ch Lk Th j S St K t ti Likh Carruthers, Tim Cheng, Luk e Theogarajan, Susanne Stemmer, Konstantin Likharev Funding: AFOSR MURI, AFOSR STTR‐II, NSF CDI
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Pattern Classification with Pattern Classification with Memristive Memristive XbarXbar CircuitsCircuits
Dmitri Strukov
UC Santa Barbara
Acknowledgments: Fabien Alibart, Elham Zamanidoost, Brian Hoskins, Gina Adam, Farnood Merrikh‐Bayat, Xinjie Guo, Ligang Gao, Christof Teuscher, John
C th Ti Ch L k Th j S St K t ti LikhCarruthers, Tim Cheng, Luke Theogarajan, Susanne Stemmer, Konstantin Likharev
Funding: AFOSR MURI, AFOSR STTR‐II, NSF CDI
UNIVERISTY OF CALIFORNIASANTA BARBARA
Motivation: SuperVision with convolutional networks
A. Krizhevsky et al, ImageNet classification with deep convolutional neural networks, NIPS’12
650,000 neurons
60,000,000 parameters
630,000,000 synapses, , y p
Backpropagation learning rule
June 2013 2Intel, Portland
UNIVERISTY OF CALIFORNIASANTA BARBARA
Motivation: SuperVision with convolutional networks
Implemented with GPUs
June 2013 3Intel, Portland
UNIVERISTY OF CALIFORNIASANTA BARBARA
Motivation: SuperVision with convolutional networks
Problem: Concurrent state‐of‐art implementations are not suitable for real‐time and low energy operation
P d l ti H b id CMOS/ i t t k ( ll d CMOL Proposed solution: Hybrid CMOS/memristor networks (so‐called CMOL CrossNets)
[1] C. Farabet et al., Large‐scale FPGA‐based convolutional networks, in: Machine Learning on Very Large Data Sets, ed. by R. Bekkerman et al., Cambridge U. Press, 2001, pp. 399‐419
[2] K. Likharev, 2012 (unpublished)
June 2013 4Intel, Portland
UNIVERISTY OF CALIFORNIASANTA BARBARA
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
5June 2013
AI–G0– G1
– G2– G9
–
Alibart et al., Nature Comm, 2013
Intel, Portland
UNIVERISTY OF CALIFORNIASANTA BARBARA
Windrow’s memistorAdaLiNe concept … … and hardware implementation
BernardWidrow
MarcianHoff
6
B. Widrow and M.E. Hoff, Jr., IRE WESCON Convention Record, 4:96 1960
June 2013 Intel, Portland
UNIVERISTY OF CALIFORNIASANTA BARBARA
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 ….