Design of a neuro-fuzzy chip using adaptive multimode approaches for an intelligent car-backing system

This paper presents a new approach to design an adaptive multimode neuro fuzzy chip (AMNFC) with on-chip learning and highly efficient resource utilization capabilities for a car-backing system. The design process is performed by a high-level datapath synthesis that is based on an optimal scheduling and a resource allocation algorithm. A novel data flow graph (DFG) scheduling algorithm suitable for parallel structure computation has been developed for designing a neuro-fuzzy chip. The proposed algorithm fulfills two major objectives. First, it simultaneously optimizes both the schedule and allocation of functional units, registers, and multiplexers with respect to a minimal cost of the hardware resources and the total time of execution. Second, it implements an adaptive multimode neural-fuzzy system with reconfiguration capability. Computer simulations and experimental results have successfully validated the effectiveness of the proposed design approach for a car-backing system.