Fast parking control of mobile robot based on multi-layer neural network on homogeneous architecture

Today, the problem of designing suitable multiprocessor architecture tailored for a target Neural Networks applications raises the need for a fast and efficient MP-SOC (MultiProcessor System-on-Chip) design environment. Additionally, the implementation of such applications on multiprocessor designs will need to exploit the parallelism and pipelining in algorithms with the hope of delivering significant reduction in execution times. To take advantage of parallelization on homogeneous multiprocessor architecture and to reduce the programming effort, we provide new MP-SOC design methodology which offers more opportunities for accelerating the parallelization of Neural Networks algorithms. The efficiency of this approach is tested on many examples of applications. This work is devoted to the design and implementation of a complete intelligent controller parking system of autonomous mobile robot based on Multi-Layer Feed-Forward Neural Networks. To emphasize some specific requirements to be considered when implementing such algorithm, we propose new parallel pipelined architecture composed of several computational stages. Additionally, we especially suggest a parallel software skeleton “SCComCM” aimed at being employed by the developed multistage architecture. The experimental results show that the proposed parallel architecture has better speed-up, less communication time, and better space reduction factor than the hand tuned hardware design.