An Efficient VHDL Implementation of two Artificial Neural Networks on Zynq-7000 FPGA

This paper presents the FPGA implementation of two different topologies of an Artificial Neural Network (ANN) on the Xilinx Zynq-7000 evaluation board. The engine dataset available in MATLAB is used to train the neural network. The resulting parameters of a neural network are taken from MATLAB and are used to implement it on FPGA. Two structures are implemented with different accuracy of sfix_24_8 and sfix_32_16 and different clock frequencies and resource utilization is measured. The maximum achievable frequency measured is 83.33 MHz and the minimum power is 0.203 W.