synaptic devices and neuron circuits for neuron … devices and neuron circuits for neuron-inspired...
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Synaptic Devices and Neuron Circuits
for Neuron-Inspired NanoElectronics
Byung-Gook Park Inter-university Semiconductor Research Center &
Department of Electrical and Computer Engineering Seoul National University
ECE & ISRC
Human vs. AlphaGo
ECE & ISRC
AlphaGo - Software
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Monte Carlo Tree Search (MCTS)
Neural Networks
<Silver, Nature (2016)>
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AlphaGo - Hardware
1920 CPU
280 GPU
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Supercomputer Performance
4
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Comparison
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Human Brain Digital Computer
5
■ neuron + synapse
■ massively parallel
■ ~ ms speed
■ low power
■ recognition/reasoning
■ self-organized
■ CPU + memory
■ serial
■ ~ ns speed
■ high power
■ computation
■ manufacturing
ECE & ISRC
Interconnection Bottleneck
Human Brain (Face Recognition) Computer System
Bottleneck!!
Bottleneck!!
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Human Brain and Its Building Blocks
Processor: neuron (~1011)
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Memory: Synapse (~1015)
7
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Neuron (1)
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Structure of a neuron
8
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Neuron (2)
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Potential change due to K+ ion channel
Structure of a neuron
9
V (mV)
t(ms) 5
50
100
0
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Electrochemical signal transmission in a synapse
Neurotransmitter is emitted in the unit of a vesicle
conveyed to the post-synaptic cell through the cleft
Synapse (1)
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Short term memory and long term memory
Synapse (2)
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Spike-timing-dependent plasticity – learning mechanism
Spike-Timing-Dependent Plasticity
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Building Blocks: Biological vs. Electronic
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Artificial Neural Network (ANN)
Concept of neural network
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** Weights are calculated by the back-propagation (BP)
method (1986).
<perceptron>
(1958)
14
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Deep Neural Network (DNN)
Multiple hidden layers
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** Vanishing gradient problem (VGP) unsupervised pre-training
(RBM) + modified activation function (ReLU)
15
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1st Breakthrough (2006)
Pre-training with restricted Boltzmann machine (RBM)
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Regard each pair of layers as RBM
']/)'(exp[
]/)(exp[)(
xx
xx
E
EP
ij
ijijW
PWW
)ln(
WxxxbxTTE
2
1)(
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2nd Breakthrough (2010)
Rectified Linear Unit (ReLU)
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** Vanishing gradient problem due to saturating
activation function
sigmoid
hyperbolic tangent
** ReLU solves the vanishing gradient problem!!
(+ Concept of signal intensity included)
ECE & ISRC
Comparison: ReLU vs. Sigmoid
<ReLU>
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<Sigmoid>
Speed of Learning: 8:1 Compression
<Original>
Epoch = 20
MSE = 0.00177
Epoch = 20
MSE = 0.00441
** MSE (mean square error)
512x512
Image
Epoch = 40
MSE = 0.00116
Epoch = 40
MSE = 0.00403
Epoch = 60
MSE = 0.00104
Epoch = 60
MSE = 0.00395
Epoch = 80
MSE = 0.00097
Epoch = 80
MSE = 0.00392
Epoch = 100
MSE = 0.00095
Epoch = 100
MSE = 0.00380
Epoch = 200
MSE = 0.00094
Epoch = 200
MSE = 0.00250
Epoch = 400
MSE = 0.00093
Epoch = 400
MSE = 0.00194
Epoch = 800
MSE = 0.00093
Epoch = 800
MSE = 0.00142
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Recurrent Neural Network (RNN)
Directed cycles in connections
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** Complicated dynamics
and difficulty in training
Sequential data modeling
19
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Long Short-Term Memory (LSTM)
RNN with LSTM
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LSTM block
** Hidden layer units with LSTM can
store and access information over
a long period of time.
20
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Spiking Neural Network (SNN) (1)
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3rd generation neural network model
- input/output: spikes
- signal intensity: firing rates
Sequence of spikes
Output
spikes
S Sequence of spikes
Sequence of spikes
Weighted sum
of decayed PSPs
V(t)
PSP(t)
Fj
t(f)j
Unit j
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Spiking Neural Network (SNN) (2)
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Learning mechanism
- error back-propagation with time coding
- spike-timing-dependent plasticity (STDP)
Spre Spost
SpreSpost
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Spiking Neural Network (SNN) (3)
Structure
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Supervised learning
23
before
learning
after
learning random fixed weights
learning with
modified rule
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STDP and Error Back-propagation
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<Bengio, arXiv.org, (2016)>
jiijW
STDP BP
jiij xxW
( : spike rate) (x : neuron output)
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Floating-body Synaptic Transistor (1)
Cross-section in A A’
direction
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Structure TEM image
Gate1Gate2
Fin
O/N/O~ 3.5/5/8 nm
BOX
O ~ 3.5 nm
Sidewall
(MTO)
50 nm
S
D
FB G2 G1
ONO
O A A’
ECE & ISRC
Floating-body Synaptic Transistor (2)
Impact-generated holes are
temporarily stored in the body.
Without further inputs, these
holes gradually disappear
through recombination
process.
Hot holes are programmed
to the floating gate.
Even without further inputs,
these charges do not
disappear without special
erasing actions.
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Short-term memorization Long-term memorization
Charge trap
layer
Charge trap
layer
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Floating-body Synaptic Transistor (3)
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0 20 40 60 80 100
0.0
0.5
1.0
1.5
Sou
rce
Cu
rren
t [
A]
Time [s]
0
2
Inp
ut
Pu
lse
Hei
ght
[V]
0 200 400 600 800 1000
Time [s]
100 μs interval 10 μs interval
Transient response of FST to spikes
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Floating-body Synaptic Transistor (4)
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10-9
10-7
10-5
10-3
10-1
0
50
100
150
Sou
rce
curr
ent
[nA
]
Time [s]10
-710
-510
-310
-1
Time [s]
N = 1~ 10
No Transition
100 μs interval
N = 1~ 10
one-time increase
per step
10 μs interval
forgetting
long-term
memory due
to hole
trapping
short-term memory
Short-term to long-term memory transition
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Floating-body Synaptic Transistor (5)
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-5 -4 -3 -2 -1 0 1 2 3 4 5-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
Sou
rce
Cu
rren
t [
A]
t [s]
STDP characteristic
Spre Spost
t
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Neuron Circuits (1)
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<spike generation part> <synaptic integration part>
Integrate-and-fire neuron circuit with capacitor integration
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Neuron Circuits (2)
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Implementation with discrete devices
<PCB layout> <Output of neuron>
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Neuron Circuits (3)
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-6 -4 -2 0 2 4 6-30
-20
-10
0
10
20
30
40
50STDP chracteristic (measuremt)
Time difference t [s]
Cu
rren
t ch
an
ge
I
[nA
]
Measured STDP characteristics
< Transient current > <STDP characteristic>
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Neuron Circuits (4)
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Integrate-and-fire neuron circuit with a floating-body MOSFET
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Neuron Circuits (5)
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Role of floating-body MOSFET in the circuit
<transient current> <spike generation>
- Temporal integration of input spike signal by the floating body
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Neuron Circuits (6)
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Shapes of spikes and STDP characteristic
<spike shapes> <STDP characteristic>
- Output and feedback spike shapes are different !!
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Integration of Neurons and Synapses
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Stacking of neuron and synapse arrays
Synapse Array
Neuron Array
Neuron Array
Synapse Array
Neuron Array
Synapse Array
Neuron Array
<primary sensory cortex> <neuromorphic system>
ECE & ISRC
q The recent advancement of ANNs has been
achieved by imitating the biological neural networks
(BNNs) more closely. Spiking neural networks with
STDP weight adjustment is the closest to the BNN.
q Combining the capacitor-less DRAM and SONOS flash
memory, we have developed floating-body synaptic transistors (FSTs), which show short- and long-term memory and STDP.
q Integrate-and-fire neuron circuits with capacitor and
floating-body integration are proposed and
implemented.
Conclusions
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ECE & ISRC B. G. Park 38
Thank you for
your attention !