Cellular Neural Networks (CNNs) are a revolutionary computing paradigm that allows for analog parallel processing similar to neural networks but where each processing unit only communicates with its nearest neighbors. CNNs consist of arrays of identical nonlinear dynamic units arranged in a grid where each unit receives input from neighboring units. The CNN Universal Machine (CNN-UM) was the first programmable analog processor array computer with its own language and operating system that was as powerful as a supercomputer for image processing applications. CNNs show promise for applications such as high-speed target recognition, visual inspection, and the development of artificial senses through cheap sensor arrays.

1. SUMBITTED BY:
MUKESH KUMAR
M.TECH(2010ECB1026)

2. Cellular Neural Network is a revolutionary concept
and an experimentally proven new computing
paradigm for analog computers. Looking at the
technological advancement in the last 50 years ;
we see the first revolution which led to pc
industry in 1980’s, second revolution led to
internet industry in 1990’s cheap sensors & mems
arrays in desired forms of artificial eyes, nose, ears
etc. this third revolution owes due to C.N.N. This
technology is implemented using CNN-UM. and
is also used in image processing. It can also
implement any Boolean functions.

3.  Cellular neural networks (CNN) are a regular, single
or multi-layer, parallel computing paradigm similar
to neural networks, with the difference that
communication is allowed between neighbouring
units only.
 processing structures with analog nonlinear dynamic
units (cells).
 Each cell is made up of linear capacitor, non linear
voltage controlled current source, resistive linear
circuit element.

4.  Cellular neural network (CNN) is a locally connected,
analog processor array which has two or more
dimensions. A standard CNN architecture consists of an
M × N rectangular array of cells C(i, j) with Cartesian
coordinate (i, j), where i = 1..M, j = 1..N

9.  The state of a cell depends on inter-connection weights
between the cell and its neighbours. These parameters
are expressed in the form of the template.

10.  The CNN Universal Machine (CNN-UM) is based on a
CNN.
 First programmable analog processor array computer with
its own language and operation system whose VLSI
implementation has the same computing power as a
supercomputer in image processing applications.
 The extended universal cells of CNN-UM are controlled by
global analogic programming unit (GAPU), which has
analog and logic parts: global analog program register,
global logic program register, switch configuration register
and global analogic control unit. Every cell has analog and
logical memory.

12.  The CNN can be defined as an M x N type array of identical
cells arranged in a rectangular grid. Each cell is locally
connected to its 8 nearest surrounding neighbors.
 Each cell is characterized by uij, yij and vij being the input,
the output and the state variable of the cell respectively.
 The output is related to the state by the nonlinear equation:
yij = f(vij) = 0.5 (| vij + 1| – |vij – 1|)

13.  High speed target recognition, tracking.
 Real time visual inspection of manufacturing
processes.
 Cheap sensors and mems arrays are in the desired
forms of artificial eyes, nose, ears, taste & realization
of telepathy.
 Intelligent vision capable of recognition of context-
sensitive & moving scenes as well as applications
requiring real time fusing of multiple modalities such
as multi spectral images involving infrared, long
wave-infrared and polarized lights.

14. PDE based in modern image processing techniques are
becoming most challenging & important for analogic
C.N.N. computers. A major challenge yet not solved by
any existing technology is to build analogic adaptive
sensor computer where sensing & computing
understanding are fully integrated on a chip.

15. THANKS