Design and implementation of a single layer feed forward neural network using stand-alone architecture FPGAs-based platform


Abstract:A single layer feed-forward neural network are proposed and implemented using theschematic editor of the Xilinx foundation series 2.1i. First the mathematical model of thedata set (weights and inputs) is presented in a matrix multiplication format. Secondly thefive design stages are presented and implemented without using the finite state machine,which control the processes of the forward propagation phase, error calculation, and thetraining algorithm. Finally the design can be optimized to decrease the total execution timeand to minimize the cost, which eventually will increase the performance and improve thefunction density.