An extended class of synaptic operators with application for efficient VLSI implementation of cellular neural networks

A synaptic operator based on multiplication requires a large amount of hardware, particularly in digital implementations. In this brief, we introduce an extended class of synaptic operators which includes the standard multiplication as a particular case. The properties of the extended class of operators are established. Among these, it was found that the global stability theorem of cellular neural networks (CNN's) is applicable to the extended class of synaptic operator as well as for the multiplier-based synapse. This is an important property which allows for the replacement of the multiplication-based synaptic operator with another specific member of the extended class, here referred to as a comparative synapse, without changing the functionality of the overall CNN system. Instead of multiplication, which has an implementation complexity of O(n/sup 2/), the comparative synapse has a complexity of only O(n) in a digital implementation (where n is the resolution of the fixed-point implementation). The effectiveness of this new operator is demonstrated by a few examples of discrete-time CNN operating in all possible dynamic modes (equilibrium, periodic and chaotic).