Investigation on MLP artificial neural network using FPGA for autonomous cart follower system

Abstract

The future of the autonomous cart follower system will be equipped with lots of sensory data, due to the ever lower cost of sensory devices. This provides design challenge on handling large data and firmware complexity. This paper investigates an alternative approach of running the autonomous cart follower system on neural network model using Field Programmable Gates Array (FPGA). A microcontroller based autonomous cart follower system is modified to use the FPGA board and implemented via the System on Chip (SOC) approach. The neural network model is trained off line then implemented as software code in the SOC. By observation the firmware footprint of the neural network model remains small size regardless of the neural network size. The result shows that with 40 % more additional resource utilization, the overall system improvement of 27 times is achieved with the usage of hardware acceleration block in SOC compared to SOC without hardware acceleration.