A Generic Architecture for Intelligent System Hardware

This paper brings out the similarities between the functional models of various intelligent systems and proposes a generic architecture for their implementation in hardware. Hardware implementation of intelligent models has proved to be efficient over software implementations and various architectures have been proposed for their implementation (Gandolfi et al., 1994). However, designing such a system is a difficult task and requires a custom implementation for the specific application. Our attempt is to provide a common hardware platform that can be programmed and used for three different intelligent approaches: fuzzy systems, neural networks and neuro-fuzzy systems. The functional similarity between all the three approaches is exploited and a generic architecture is proposed. After ascertaining the best approach for the problem at hand, the system can be programmed accordingly. Through computer simulation of the proposed architecture, targeting a Xilinx CoolRunner device, we show that the system performs well in all modes of operation