Photonic Reservoir Computing · P. HOTONICS. R. ESEARCH. G. ROUP. 44. Computational complexity...
Transcript of Photonic Reservoir Computing · P. HOTONICS. R. ESEARCH. G. ROUP. 44. Computational complexity...
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Photonic reservoir computing using silicon chips
Kristof Vandoorne, Pauline Mechet, Martin Fiers, Thomas Van Vaerenbergh, Bendix Schneider, Andrew Katumba, Floris Laporte, David Verstraeten, Benjamin Schrauwen, Joni Dambre and Peter Bienstman
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THE BLACK BOX
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What can this chip do?
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Several things!
• Do arbitrary boolean calculations with memory on a bitstream
• Recognise arbitrary 5-bit headers at 12.5 Gbps
• Perform speech recognition of isolated digits
• Does not consume any active power
• Easily upscalable to higher speeds
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How does it do it?
Using “Reservoir computing”, a brain-inspired technique to solve pattern recognition problems in a fast and power-efficient way
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WHAT IS RESERVOIR COMPUTING?
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What is reservoir computing?
• From field of machine learning (2002)
• Related to neural networks
• So far mainly in software
• Very successful:• Better than state-of-the-art digit recognition
• Speech recognition
• Robot control
• …
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Reservoir Readout
Reservoir computing
Don’t train the neural network, only train the linear readout
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reservoir state
readout
reservoir
nothing pebbles gritpebbles
grit
A hardware implementation…
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To higher order space
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Why does it work?
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PHOTONIC RESERVOIR COMPUTING
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Photonics
Photonic reservoirs
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• Faster• More power efficient• Richer dynamics in nodes• Light has a phase
Why photonics?
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OPTICAL AMPLIFIER NETWORKSThe very beginning…
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Looks like tanh, but positive signals only
Output
Use SOAs as neurons
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The gain in the SOA model is dependent on the input power and its own history
SOA model
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81 SOAs
Swirl topology
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5 female speakers, saying
10 times the same 10 digits,
ranging from zero to nine
Speech corpus
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• dynamics of light signal should be on time scale of SOA dynamics and chip delays
• convert 1 sec speech to 1 ns light signal
• 9 orders of magnitude upconversion
Time scales
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Word error rate
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Optimal delay
75 ps
187.5 ps
312.5 ps
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Absolute minimum
(phase controlled)Minimum
(phase averaged)
Reducing 2D plots to single number
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Controlling the phase offers clear advantage
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PASSIVE SILICON RESERVOIRSThe next step…
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What happens if you remove the SOAs?
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Passive Silicon reservoir
• silicon photonics: mature technology
• nodes become simple splitters/combiners
• non-linearity in readout suffices
• no need for amplifiers which consume power
• no longer limited by timescale of non-linearity
Vandoorne et al, Nature Comms, 5, 3541, 2014
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NL coming from the detector suffices!
Speech task: passive reservoirs (no amplifiers)
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16 node swirl network where 11 nodes could be measured from 1 input
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The input: 11136 bits modulated at 1531 nm with speeds between 125Mbit/s and 12.5Gbit/s
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First task: desired output should be the XOR of every bit with the previous bit.
Hard task in machine learning (non-linear!)
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Measurements and simulations for the XOR task correspond
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The XOR task can be solved at different speeds and different bit combinations
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Other Boolean tasks can be solved as well (with the same reservoir states)
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Header recognition
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Advantages
• Scalability: • Note that we spent a lot of effort to slow down the signal!
• Easily scalable to higher speeds by shortening the delays
• No active power consumption on chip
• Same generic chip can be used for• digital tasks (simulation confirmed by experiment)
• analog tasks (theory only, no suitable equipment)
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APPLICATIONS
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Telecom task: non-linear equalization of optical links
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Signal Equalization: Results….
Up to 200 km below FEC Limit
Metro Links
Equalization results with passive SOI chip
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Scaling this up
• PhResCo: recently started H2020 European project (KULeuven, IBM, UGent, Supelec, IHP)
• Integrated readout on chip:
out…
…
…
…in
reservoir readout
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First design: comparing 3 different technologies
2 x 9 Reservoir
BTO Test Structures
Si Readout BTO Readout
VO2 Readout
VO2 Test Structures
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Conclusions
Neuromorphic computing
is interesting new paradigm
for photonics information processing
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Flow cytometry
http://www.lifetechnologies.com
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Imec cell sorter
Integrated micro-fluidic
channels
On-chip high speed
cell sorting
On-chip Fast
high-resolution microscopy
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Computational complexity
➢ Complex convolution or sequence of 2D FFTs
➢ 512x512 pixels/image
➢ 1M cells/sec
➢ 48.8M Flops for reconstruction
➢ ~ 60 TFlops/sec including classification
http://www.top500.org/
# Site System Cores Perf.ormance[TF/sec]
Power [kW]
482 AutomotiveUnited States
IBM Flex System x240, Xeon E5-2670 8C 2.600GHz, Infiniband FDR IBM
8,336 157.7 181
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Algorithm Methods
classificationfeature
selectionnumerical
reconstruction
Lymphocytes
Monocytes
Granulocytes
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Real experimental data
k
1.39
1.37
1.34E
Direction of flow
Incident plane wave Scattered wave +
Direct wave
Detector plane
Microfluidic flow
chamber
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Neural network - pipeline
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Input Layer Hidden Layer Output Layer
ANN: < 200 GigaFlop/sec !
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Three-part WBC classification Results
• Dataset of ~7500 non-purified WBC:
Granulocytes (59.8%),
Lymphocytes (34.6%),
Monocytes (5.6%)
• Use of 10 random folds for cross-
validating (CV) the results
• Adding noise to weights at fixed SNR
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Purified monocyte/granulocyte classification
Averaged classification results with increasing signal-to-noise ratio (from left to right: 30dB, 10 dB, 3 dB)
Class 1 = monocytesClass 2 = granulocytes
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Towards a hardware solution
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Conclusions
Neuromorphic computing
is interesting new paradigm
for photonics information processing
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EXCITABLE SILICON RINGS
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Building a photonic spiking neuron
= ?
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Research question
• People have seen excitability in photonics before, but never cascaded it on chip
• Can we cascade excitability on-chip using ring-resonator neurons?
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Thermo-optic effect causes redshift
Light circulation in ring resonance dip/peak
Heating of the ring redshiftT
ire
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Self-heating causes bistability
Light circulation in ring resonance dip/peak
Heating of the ring redshiftT
ire
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Free carriers cause blueshift
Light circulation in ring resonance dip/peak
Free carriers blueshift
ire
N
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Combination free carrier and thermal effect can cause self-pulsation
Light circulation in ring ~ ps
Cooling of the ring ~ 100 ns
Free carriers ~ ns
ire
N
T
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Simulations: bistability and self-pulsation
Q 6.25 104
25 pm62 pm
R 4 μm
dB3
r −
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Simulation: excitability
Wavelength and input power ‘near’ self-pulsation...
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Simulation: cascadability
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Experiment: self-pulsation
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Experiment: excitability
Pulses excited by external trigger signal:
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Experiment: cascadability
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Cascading rings = creating a delay line
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t
t
t
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tt
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Cascading rings = creating a delay line
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t
t
t
t
tt
t
Max ~ 9-10 rings
t
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10 rings result in a ~200 ns delay of a 15-20 ns pulse
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10 rings result in a ~200 ns delay of a 15-20 ns pulse
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Making a loop => spike encoded memory/clock
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t
t t
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If delay > internal timescale neuron
=> Excitation loops through rings
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The concept works! (loop from ring 2-8)
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Conclusions
Neuromorphic computing
is interesting new paradigm
for photonics information processing