Title :
A new cell output nonlinearity for dense cellular nonlinear network integration
Author :
Paasio, Ari ; Halonen, Kari
Author_Institution :
Electron. Circuit Design Lab., Helsinki Univ. of Technol., Espoo, Finland
fDate :
3/1/2001 12:00:00 AM
Abstract :
A new approach for designing a cellular nonlinear network (CNN) cell is introduced. The method can be used when the processed images are bipolar, i.e., black and white. A simple circuit realization is introduced that can be applied when building up the analog part of the cell. The cell output nonlinearity is an easily realizable positive range high gain sigmoid. Moreover, a state limited model is adopted to decrease the complexity of the design. The coefficient building blocks are simple, and also because these blocks introduce an almost error free multiplication by zero, the corresponding devices can be made relatively small due to relaxed coefficient accuracy requirements. This approach yields a very small cell area on silicon and can be used with inexpensive digital CMOS processes. Simulation results are given for both static and dynamic behavior of the proposed structure. The dynamic simulation shows very fast convergence time compared to other reported approaches for CNN very large scale integration implementation
Keywords :
CMOS analogue integrated circuits; VLSI; cellular neural nets; neural chips; CMOS VLSI; analog circuit; bipolar image processing; cell output nonlinearity; cellular nonlinear network; coefficient multiplier; dynamic simulation; positive range high gain sigmoid; state limited model; static simulation; CMOS process; CMOS technology; Cellular networks; Cellular neural networks; Convergence; Electronic circuits; Helium; Nonlinear equations; Silicon; Very large scale integration;
Journal_Title :
Circuits and Systems I: Fundamental Theory and Applications, IEEE Transactions on