CNN Survey Presentation
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Transcript of CNN Survey Presentation
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7/30/2019 CNN Survey Presentation
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Cellular Neural Networks
Survey of Techniques and Applications
Max Pflueger
CS 152: Neural Networks
December 12, 2006
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Cellular Neural Networks
Cells are given aspatial arrangementwith connectionbetween cells that arewithin a certain radiusof each other
All the cells within theradius of cell (i,j) arethe neighborhood ofcell (i,j)
(a) neighborhood with r = 1
(b) r = 2
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Templates
Cell behavior is governed by thedifferential equation shown above
A template specifies values forA, B,and zthat will be used throughout the
CNN to achieve some effect A, and B, are typically matrices of
weights associated with the relativeposition of neighbors
ijjiSlkC
kljiSlkC
klijij zulkjiBylkjiAxxrr
),(),(),(),(
),;,(),;,(
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The CNN Universal Machine
CNN with the ability to change
templates during operation
Templates can be strung together,creating a programmable CNN
Instructions are similar to traditional
microprocessor
Turing complete
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Application of CNNs and the CNN-UM
Ocean modeling
10,000 fps image recognition
Bionic eye
Face and eye detection
Template learning
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Ocean Modeling
Exact solutions to fluid mechanics
problems require solving systems of
partial differential equations Analytical solutions do not exist in most
cases
Numerical solutions are verycomputationally intensive
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Ocean Modeling
Nagy and Szolgay designeda simulation of a CNN-UMwith modified cell architecture
to model ocean currents Simulation was run on a mid-
size FPGA and an Athlon XP1800+ for comparison
Athlon XP 1800+: 56 min
FPGA: 41 s
A larger FPGA could do thecalculation in ~1 sec
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Template Learning
It would be nice to use learning
techniques to find useful templates for
CNNs Gradient descent is promising, except
that it is difficult to compute the gradient
for a CNN
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Template Learning
Brendel, Roska, and Bartfai presented theequations for calculating the gradient of aCNN
They also showed that these equations havethe same neighborhood and connectivity asthe original CNN
Therefore, a CNN-UM can be used tocompute the gradients for templates, makingit possible to do fast on-line training with aCNN-UM
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Face and Eye Detection
Detecting faces in imagesis a classic problem incomputer science
Balya and Roska designeda CNN algorithm forrecognizing andnormalizing faces fromcolor images. Accurate
Runs in hardware, so it isvery fast
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References
Chua, Leon O. and Tams Roska. Cellular Neural Networksand Visual Computing. Cambridge: Cambridge UniversityPress, 2002.
Nagy, Z.; Szolgay, P., "Emulated digital CNN-UMimplementation of a barotropic ocean model," Neural Networks,2004. Proceedings. 2004 IEEE International Joint Conferenceon , vol.4, no.pp. 3137- 3142 vol.4, 25-29 July 2004
Brendel, M., Roska, T., and Brtfai, G. 2002. GradientComputation of Continuous-Time Cellular Neural/Nonlinear
Networks with Linear Templates via the CNN UniversalMachine. Neural Process. Lett. 16, 2 (Oct. 2002), 111-120.
Balya, D. and Roska, T. 1999. Face and Eye Detection by CNNAlgorithms. J. VLSI Signal Process. Syst. 23, 2-3 (Nov. 1999),
497-511.