VLSI Implementation of Neural Networks

Currently, fuzzy controllers are the most popular choice for hardware implementation of complex control surfaces because they are easy to design. Neural controllers are more complex and hard to train, but provide an outstanding control surface with much less error than that of a fuzzy controller. There are also some problems that have to be solved before the networks can be implemented on VLSI chips. First, an approximation function needs to be developed because CMOS neural networks have an activation function different than any function used in neural network software. Next, this function has to be used to train the network. Finally, the last problem for VLSI designers is the quantization effect caused by discrete values of the channel length (L) and width (W) of MOS transistor geometries.

• Type: Journal
• Year: 2000
• Where: IJNS
• Authors: Bogdan M. Wilamowski, J. Binfet, M. O. Kaynak
• Keywords: CMOS Neural Networks, Fuzzy Controller, IJNS 2000, Neural Network