Photonic Neuromorphic computing
Presenter: Rafatul Faria
PhD student, ECE, Purdue UniversityMajor: Micro and Nanoelectronics (MN)
Outline
General Overview of Neuromorphic
computing
Photonic Devices for Neuromorphic
computing
What is neuromorphic computing and why is it important?
Basics of neurons and synapses, learning
Different technologies targeting neuromorphic computing
Different photonic devices for neuromorphic computing
Pros and cons
Future directions
Outline
General Overview of Neuromorphic
computing
Photonic Devices for Neuromorphic
computing
What is neuromorphic computing and why is it important?
Basics of neurons and synapses, learning
Different technologies targeting neuromorphic computing
Different photonic devices for neuromorphic computing
Pros and cons
Future directions
What is neuromorphic computing?
https://www.openpr.com/news/371546
Neuromorphic computing
Mimicking human brain function for low energy, high speed cognitive computing and learning
smart phones, sensor networks, self-driving automobiles, robots, public safety, medical imaging, real-time video analysis, signal processing, olfactory detection, and digital pathology and so on …
• human brain contains over 100 billion neurons and 100 trillion to 150 trillion synapses.
• Power consumption: roughly 20 Watts!!!
Why is neuromorphic computing important?
Building an efficient neuromorphic chip
IBM trueNorth (2014) (DARPA funded custom hardware)
GPU vendors Nvidia, AMD Google:
Tensor Processing unit (TPU)
Mircosoft
IntelLoihi
(2017)
one million programmable “neurons” and 256 million “synapses”http://www.ibtimes.com/ibm-creates-cognitive-chip-mimics-human-brain-1652858
https://singularityhub.com/2017/09/29/intels-new-brain-like-chip-will-learn-on-the-fly/
Apple
Why is neuromorphic computing important?
Building an efficient neuromorphic chip
IBM trueNorth (2014) (DARPA funded custom hardware)
GPU vendors Nvidia, AMD Google:
Tensor Processing unit (TPU)
Mircosoft
IntelLoihi
(2017)
one million programmable “neurons” and 256 million “synapses”http://www.ibtimes.com/ibm-creates-cognitive-chip-mimics-human-brain-1652858
https://singularityhub.com/2017/09/29/intels-new-brain-like-chip-will-learn-on-the-fly/
Apple
https://www.engadget.com/2016/03/14/the-final-lee-sedol-vs-alphago-match-is-about-to-start/
Google DeepMind AI program AlphaGo (March 2016)
Neural Network: neurons and synapsesBiological neuron
Diep et al., APL, 2014
Modeling a simple Perceptron neuron
Mathematical operations:✓ Multiplication✓ summation
Neural Network: neurons and synapsesBiological neuron
Diep et al., APL, 2014
Modeling a simple Perceptron neuron
Mathematical operations:✓ Multiplication✓ summation
Neural Network: neurons and synapsesBiological neuron
Diep et al., APL, 2014
Modeling a simple Perceptron neuron
Artificial neural network (ANN)
Mathematical operations:✓ Multiplication✓ summation
Neural Network: neurons and synapses
Membrane potential
Biological neurons are stochastic in nature
Burkitt, Anthony N. "A review of the integrate-and-fire neuron model: I. Homogeneous synaptic input." Biological cybernetics 95.1 (2006): 1-19.
Stochastic spiking neuron
Training a Neural Network
Most widely used learning
mechanism: Back propagation
Nature, vol. 323, 1986
Beyond CMOS devices to mimic neuronMoores law ending
Beyond CMOS devices to mimic neuron
CMOS based implementation of neural network: Von neuman bottleneck
Moores law ending
• Low bandwidth between CPU and memory
• Majority power loss in data transfer
process
Beyond CMOS devices to mimic neuron
CMOS based implementation of neural network: Von neuman bottleneck
Moores law ending
Neuromorphic computing
2682 references!!!
• Low bandwidth between CPU and memory
• Majority power loss in data transfer
process
Outline
General Overview of Neuromorphic
computing
Photonic Devices for Neuromorphic
computing
What is neuromorphic computing and why is it important?
Basics of neurons and synapses, learning
Different technologies targeting neuromorphic computing
Different photonic devices for neuromorphic computing
Pros and cons
Future directions
Why Photonic neuromorphic computing?
Lecture 8ECE 695
Nanophotonics and Metamaterials
Why Photonic neuromorphic computing?
Lecture 8ECE 695
Nanophotonics and Metamaterials
Photonic devices can potentially meet all these criteria
Required properties of devices for neumorphic computing:• High connectivity for parallel operation• Low power• Faster computing• Collocating memory and processing• Lower footprint area, scalable
Photonic Spiking neuron
Schematic and operation of a LIF neuron
Photonic Spiking neuron
Schematic and operation of a LIF neuron Spiking neuron properties
Photonic Spiking neuron
Schematic and operation of a LIF neuron Spiking neuron properties
Potential photonic elements for fabricating LIF neuron
First bench-top model for photonic neuron (LIF neuron)
Carrier modulationnonlinear optical
loop mirror (NOLM)
Spiking neuron (continued)
1
G: Variable attenuator
T: Tunable delay line
: low power pulse train
SOA: Semiconductor Optical Amplifier
First bench-top model for photonic neuron (LIF neuron)
Drawbacks:• Performs integration and thresholding (2 required properties
out of 5). Lacks reset condition, ability to generate pulses and truly asynchronous behavior.
• Fiber based neuron, larger footprint area, not scalable
Carrier modulationnonlinear optical
loop mirror (NOLM)
Spiking neuron (continued)
1
G: Variable attenuator
T: Tunable delay line
: low power pulse train
SOA: Semiconductor Optical Amplifier
Simple application using previous bench-top fiber based neuron model
Spiking neuron (continued)
Barn Owl Auditory Localization
Simple application using previous bench-top fiber based neuron model
Spiking neuron (continued)
Barn Owl Auditory Localization
Input signals far apart: NO output spike
Input signals close: output spikes
Generalized model
Excitable Laser Neuron
( ) : Gain
( ) : Absorption
( ) : Laser intensity
: Bias current of the gain
: Level of absorption
: differential absorption relative to differential gain
: Relaxation rate of gain
: Relaxation rate of
G
Q
G t
Q t
I t
A
B
a
absorber
: inverse photon lifetime
( ) : small contribution to intensity due to noise
I
f G
Rate equations:
Smaller footprint area, scalable ☺
Generalized model
Excitable Laser Neuron
( ) : Gain
( ) : Absorption
( ) : Laser intensity
: Bias current of the gain
: Level of absorption
: differential absorption relative to differential gain
: Relaxation rate of gain
: Relaxation rate of
G
Q
G t
Q t
I t
A
B
a
absorber
: inverse photon lifetime
( ) : small contribution to intensity due to noise
I
f G
Rate equations:
Threshold condition:
( ) ( ) 1G t Q t
VCSEL Neuron
VCSEL: Vertical Cavity Surface Emitting Laser
VCSEL Neuron
VCSEL: Vertical Cavity Surface Emitting Laser
• Scalable • Low power
Silicon photonic weight bank
Tait, Alexander N., et al. "Neuromorphic photonic networks using silicon photonic weight banks." Scientific Reports 7.1 (2017): 7430.
MRR: Microring resonatorBPD: Balanced photo diodeLD: Laser diodeMZM: Mach-Zehnder modulator (neuron)WDM: Wavelength-division-multiplexerAWG: Arrayed waveguide grating
Microring resonator as weight bank
Neuron 4
Neuron 1 Experimental set up
Neuron 4
• Weight logic implemented by tunable microring resonator (MRR)
• Hybrid approach: optical+electrical
Silicon photonic weight bank
Tait, Alexander N., et al. "Neuromorphic photonic networks using silicon photonic weight banks." Scientific Reports 7.1 (2017): 7430.
MRR: Microring resonatorBPD: Balanced photo diodeLD: Laser diodeMZM: Mach-Zehnder modulator (neuron)WDM: Wavelength-division-multiplexerAWG: Arrayed waveguide grating
Microring resonator as weight bank
Neuron 4
Neuron 1 Experimental set up
Neuron 4
Shen, Yichen, et al. "Deep learning with coherent nanophotonic circuits." Nature Photonics (2017)
Fully optical neural network• Fully optical neural network (ONN)
• ONN composed of OpticalInterference unit (OIU) and Optical nonlinearity unit (ONU).
• OIU implements any real-valued matrix multiplication by using optical beam-splitters, phase shifters and attenuators.
• ONU can be implemented using common optical non-linearity such as saturable absorption e.g. Grphene saturable absorber.
• This scheme is experimentally demonstrated within a subset of a programable nanophotonic processor (PNP)- a silicon photonic integrated circuit fabricated in the OPSIS foundry.
Fully optical
Fully optical neural network (continued)
Two layer neural network for vowel recognition
Co
rrel
atio
n m
atri
ces
2000 node coherent Ising machine with all-to-all connections
All to all connection by FPGA module
https://en.wikipedia.org/wiki/Maximum_cut
Max-Cut problem
Inagaki, Takahiro, et al. "A coherent Ising machine for 2000-node optimization problems." Science 354.6312 (2016): 603-606.
Summary• A very blooming field which is already shaping our daily life.
• Many different areas are trying to come up with the best implementation using many different physics.
• Photonic application is very promising for low power high speed computing and transfer of data and may eventually be the winner.
Neuromorphic computing
electronics
spintronics
Photonics/plasmonics
Ultimate goal: low power, high speed brain like computing
Thank you ☺
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