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Photonic Neuromorphic computing Presenter: Rafatul Faria PhD student, ECE, Purdue University Major: Micro and Nanoelectronics (MN)

Photonic Neuromorphic computing - nanoHUB

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Page 1: Photonic Neuromorphic computing - nanoHUB

Photonic Neuromorphic computing

Presenter: Rafatul Faria

PhD student, ECE, Purdue UniversityMajor: Micro and Nanoelectronics (MN)

Page 2: Photonic Neuromorphic computing - nanoHUB

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

Page 3: Photonic Neuromorphic computing - nanoHUB

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

Page 4: Photonic Neuromorphic computing - nanoHUB

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!!!

Page 5: Photonic Neuromorphic computing - nanoHUB

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

Facebook

Page 6: Photonic Neuromorphic computing - nanoHUB

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

Facebook

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)

Page 7: Photonic Neuromorphic computing - nanoHUB

Neural Network: neurons and synapsesBiological neuron

Diep et al., APL, 2014

Modeling a simple Perceptron neuron

Mathematical operations:✓ Multiplication✓ summation

Page 8: Photonic Neuromorphic computing - nanoHUB

Neural Network: neurons and synapsesBiological neuron

Diep et al., APL, 2014

Modeling a simple Perceptron neuron

Mathematical operations:✓ Multiplication✓ summation

Page 9: Photonic Neuromorphic computing - nanoHUB

Neural Network: neurons and synapsesBiological neuron

Diep et al., APL, 2014

Modeling a simple Perceptron neuron

Artificial neural network (ANN)

Mathematical operations:✓ Multiplication✓ summation

Page 10: Photonic Neuromorphic computing - nanoHUB

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

Page 11: Photonic Neuromorphic computing - nanoHUB

Training a Neural Network

Most widely used learning

mechanism: Back propagation

Nature, vol. 323, 1986

Page 12: Photonic Neuromorphic computing - nanoHUB

Beyond CMOS devices to mimic neuronMoores law ending

Page 13: Photonic Neuromorphic computing - nanoHUB

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

Page 14: Photonic Neuromorphic computing - nanoHUB

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

Page 15: Photonic Neuromorphic computing - nanoHUB

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

Page 16: Photonic Neuromorphic computing - nanoHUB

Why Photonic neuromorphic computing?

Lecture 8ECE 695

Nanophotonics and Metamaterials

Page 17: Photonic Neuromorphic computing - nanoHUB

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

Page 18: Photonic Neuromorphic computing - nanoHUB

Photonic Spiking neuron

Schematic and operation of a LIF neuron

Page 19: Photonic Neuromorphic computing - nanoHUB

Photonic Spiking neuron

Schematic and operation of a LIF neuron Spiking neuron properties

Page 20: Photonic Neuromorphic computing - nanoHUB

Photonic Spiking neuron

Schematic and operation of a LIF neuron Spiking neuron properties

Potential photonic elements for fabricating LIF neuron

Page 21: Photonic Neuromorphic computing - nanoHUB

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

Page 22: Photonic Neuromorphic computing - nanoHUB

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

Page 23: Photonic Neuromorphic computing - nanoHUB

Simple application using previous bench-top fiber based neuron model

Spiking neuron (continued)

Barn Owl Auditory Localization

Page 24: Photonic Neuromorphic computing - nanoHUB

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

Page 25: Photonic Neuromorphic computing - nanoHUB

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:

Page 26: Photonic Neuromorphic computing - nanoHUB

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

Page 27: Photonic Neuromorphic computing - nanoHUB

VCSEL Neuron

VCSEL: Vertical Cavity Surface Emitting Laser

Page 28: Photonic Neuromorphic computing - nanoHUB

VCSEL Neuron

VCSEL: Vertical Cavity Surface Emitting Laser

• Scalable • Low power

Page 29: Photonic Neuromorphic computing - nanoHUB

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

Page 30: Photonic Neuromorphic computing - nanoHUB

• 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

Page 31: Photonic Neuromorphic computing - nanoHUB

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

Page 32: Photonic Neuromorphic computing - nanoHUB

Fully optical neural network (continued)

Two layer neural network for vowel recognition

Co

rrel

atio

n m

atri

ces

Page 33: Photonic Neuromorphic computing - nanoHUB

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.

Page 34: Photonic Neuromorphic computing - nanoHUB

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

Page 35: Photonic Neuromorphic computing - nanoHUB

Thank you ☺