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 imageprocessing.It can also implement any Boolean functions.

**ARCHITECTURE OF CNN**

A standard CNN architecture consists of an m*n rectangular array of cells c(i,j) with Cartesian coordinates (i,j) i=1,2…..M, j=12…...N.

A class -1 m*n standard CNN is defined by a m*n rectangular array of cells cij located at site (i,j) i= 1,2 …….m ,j=1,2,….n is defined mathematically by

(dXij/dt )= -Xij + A(I,j,k,l) Ykl + B(i,j,k,l) + Zij

**DIGITAL HARDWARE ACCELERATORS **

We can emulate analog dynamics by digital hardware accelerators. Emulating large CNN arrays need more computing power. A special hardware accelerator board (HAB) was developed for simulating up to one million pixel arrays with on board memory, with 4 DSP chips. Using hab's large arrays can be simulated with cheap pc. Actually the DSP is a reduced instruction set (RISC). Processor used for calculating CNN dynamics

New dsp packages host 4-8 dsp processors in a chip .hence the process numbers are 4-8 times higher .since for the calculation of CNN dynamics ,a major part of dsp is not used , a special purpose chip (castle) have been developed