DLP

1
DLP-Driven, Optical Neural Network Results and
Future Design

• Emmett Redd
• Professor
• Missouri State
• University

2
Neural Network Applications

• Stock Prediction Currency, Bonds, SP 500,
  Natural Gas
• Business Direct mail, Credit Scoring,
  Appraisal, Summoning Juries
• Medical Breast Cancer, Heart Attack Diagnosis,
  ER Test Ordering
• Sports Horse and Dog Racing
• Science Solar Flares, Protein Sequencing,
  Mosquito ID, Weather
• Manufacturing Welding Quality, Plastics or
  Concrete Testing
• Pattern Recognition Speech, Article Class.,
  Chem. Drawings
• No Optical ApplicationsWe are starting with
  Boolean
• most from www.calsci.com/Applications.html

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Optical Computing Neural Networks

• Optical Parallel Processing Gives Speed
• Lenslets Enlight 2568000 Giga Multiply and
  Accumulate per second
• Order 1011 connections per second possible with
  holographic attenuators
• Neural Networks
• Parallel versus Serial
• Learn versus Program
• Solutions beyond Programming
• Deal with Ambiguous Inputs
• Solve Non-Linear problems
• Thinking versus Constrained Results

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Optical Neural Networks

• Sources are modulated light beams (pulse or
  amplitude)
• Synaptic Multiplications are due to attenuation
  of light passing through an optical medium (30
  fs)
• Geometric or Holographic
• Target neurons sum signals from many source
  neurons.
• Squashing by operational-amps or nonlinear optics

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Standard Neural Net Learning

• We use a Training or Learning algorithm to adjust
  the weights, usually in an iterative manner.

y1
x1 xN
S



yM
S
Target Output (T)
Other Info
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FWL-NN is equivalent to a standard Neural Network
Learning Algorithm
FWL-NN

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Optical Recurrent Neural Network
Signal Source (Layer Input)
Target Neuron Summation
Synaptic Medium (35mm Slide)
Postsynaptic Optics
Presynaptic Optics
Squashing Functions
Micromirror Array
Recurrent Connections
Layer Output
A Single Layer of an Optical Recurrent Neural
Network. Only four synapses are shown. Actual
networks will have a large number of synapses. A
multi-layer network has several consecutive
layers.
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Definitions

• Fixed-Weight Learning Neural Network (FWL-NN) A
  recurrent network that learns without changing
  synaptic weights
• Potency A weight signal
• Tranapse A Potency modulated synapse
• Planapse Supplies Potency error signal
• Zenapse Non-Participatory synapse
• Recurron A recurrent neuron
• Recurral Network A network of Recurrons

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Optical Fixed-Weight Learning Synapse
Tranapse
x(t)
y(t-1)
S
W(t-1)
T(t-1)
x(t-1)
Planapse
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Page Representation of a Recurron
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Optical Neural Network Constraints

• Finite Range Unipolar Signals 0,1
• Finite Range Bipolar Attenuation-1,1
• Excitatory/Inhibitory handled separately
• Limited Resolution Signal
• Limited Resolution Synaptic Weights
• Alignment and Calibration Issues

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Optical System
DMD or DLP
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Optical Recurrent Neural Network
Signal Source (Layer Input)
Target Neuron Summation
Synaptic Medium (35mm Slide)
Postsynaptic Optics
Presynaptic Optics
Squashing Functions
Micromirror Array
Recurrent Connections
Layer Output
A Single Layer of an Optical Recurrent Neural
Network. Only four synapses are shown. Actual
networks will have a large number of synapses. A
multi-layer network has several consecutive
layers.
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Design Details
and Networks

• Digital Micromirror Device
• 35 mm slide Synaptic Media
• CCD Camera
• Synaptic Weights - Positionally Encoded
• - Digital Attenuation
• Allows flexibility for evaluation.

Recurrent AND Unsigned Multiply FWL Recurron
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DMD/DLPA Versatile Tool

• Alignment and Distortion Correction
• Align DMD/DLP to CCDPEGS
• Align Synaptic Media to CCDHOLES
• Calculate DMD/DLP to Synaptic Media
  AlignmentPutting PEGS in HOLES
• Correct projected DMD/DLP Images
• Nonlinearities

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Stretch, Squash, and Rotate
1 x1y0 x0y1 x2y0 x1y1 x0y2 x3y0 x2y1
x1y2 x0y3 etc.
None Linear Quadratic Cubic etc.
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Where We Are and Where We Want to Be
C
C'
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CCD Image of Known DLP Positions
Automatically Finds Points via Projecting 42
Individual Pegs C H ? D H C ? DP
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CCD Image of Holes in Slide Film
Manually Click on Interference to Zoom In
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Mark the Center
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Options for Automatic Alignment

• Make holes larger so diffraction is reduced
• A single large Peg might illuminate only one Hole
  at a time
• Maximum intensity would mark Hole location
• Fit ellipse to 1st minimum Ax2BxyCy2DxEy1
  0
• Find its center xc(BE-2CD)/(4AC-B2),
  yc(BD-2AE)/(4AC-B2) Hole location

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Ellipse Fitting
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Eighty-four Clicks Later
C' M ? C M C' ? CP
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DLP Projected Regions of Interest
D' H-1?M-1?H?D and C' H ? D'
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Nonlinearities
Measured light signals vs. Weights for FWL
Recurron Opaque slides arent, 6 leakage.
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Neural Networks and Results

• Recurrent AND
• Unsigned Multiply
• Fixed Weight Learning Recurron

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Recurrent AND
IN
IN ? IN-1
IN-1
1-cycle delay
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Recurrent AND Neural Network
Source Neurons (buffers)
Bias (1)
-14/16
logsig(16S)
10/16
1
IN
S
IN ? IN-1
10/16
0
Terminal Neurons
IN-1
-1/2
linsig(2S)
S
1
2/2
0
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Synaptic Weight Slide
Weights 0.5 10/16 -1/2 -14/16 10/16
2/2
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Recurrent AND Demo

• MATLAB

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Pulse Image (Regions of Interest)
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Output Swings Larger than Input
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Synaptic Weight Slide
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Unsigned Multiply Results
About 4 bits Blue-expected Black-obtained Red-sq
uared error
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Page Representation of a Recurron
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FWL Recurron Synaptic Weight Slide
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Optical Fixed-Weight Learning Synapse
Tranapse
x(t)
y(t-1)
S
W(t-1)
T(t-1)
x(t-1)
Planapse
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Optical Recurrent Neural Network
Signal Source (Layer Input)
Target Neuron Summation
Synaptic Medium (35mm Slide)
Postsynaptic Optics
Presynaptic Optics
Squashing Functions
Micromirror Array
Recurrent Connections
Layer Output
A Single Layer of an Optical Recurrent Neural
Network. Only four synapses are shown. Actual
networks will have a large number of synapses. A
multi-layer network has several consecutive
layers.
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Future Integrated Photonics

• Photonic (analog)
• i. Concept
• a. Neuron
• ß. Weights
• ?. Synapses

Photonics Spectra and Luxtera
40
Continued

• ii. Needs
• a. Laser
• ß. Amplifier (detectors and control)
• ?. Splitters
• d. Waveguides on Two Layers
• e. Attenuators
• ?. Combiners
• ?. Constructive Interference
• ?. Destructive Interference
• ?. Phase

Photonics Spectra and Luxtera
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Interest Generated?

• We wish to implement Optical Neural Networks in
  SiliconIncluding Fixed Weight Learning.
• To do so, we need Collaborators.
• Much research remains, but an earlier start means
  an earlier finish.
• Please contact me if interested.

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DLP-Driven, Optical Neural Network Results and
Future Design

• Emmett Redd A. Steven Younger
• Missouri State University
• EmmettRedd_at_MissouriState.edu

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Source Pulse