cnn survey presentation

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.

cnn survey presentation

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